Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Definitions

• This disclosure relates to a parameter adjustment device, a parameter adjustment method, and a program.
• DC-DC converters used in electric vehicles and other devices multiple feedback controls are combined to achieve complex and precise current and voltage control in order to control charging and discharging according to driving conditions.
• production equipment used in factories e.g., component mounters, assembly robots, etc.
• multiple servo-controlled motors are combined to achieve complex and precise operation control.
• the operation of the controlled object is controlled according to a large number of control parameters. Users empirically adjust (i.e., optimize) the control parameters to obtain the desired performance for the controlled object's operation.
• AS3 Adaptive Scenario Subset Selection
• Patent Document 1 uses optimization that focuses on bottleneck operating conditions, which can result in an extremely long time required to adjust the control parameters.
• Non-Patent Document 1 may actively execute and evaluate such operating conditions, which may result in a very long time being required to adjust the control parameters.
• the present disclosure therefore aims to provide a parameter adjustment device, parameter adjustment method, and program that can reduce the time required for performance evaluation using simulations or actual equipment when automatically adjusting control parameters for multiple operating conditions.
• a parameter adjustment device that searches for optimal control parameters for operating a control system under multiple operating conditions by operating the control system while adjusting control parameters, and includes: a condition setting unit that sets at least one operating condition of the multiple operating conditions and the control parameter in the control system; an evaluation unit that calculates, for each of the at least one operating condition, an evaluation value related to the operation of the control system when the control system operates under the at least one operating condition and the control parameter set by the condition setting unit; a comprehensive evaluation unit that calculates, as an overall evaluation value, a weighted average obtained using at least the maximum and minimum values of the evaluation values calculated by the evaluation unit; and an optimization unit that uses an optimization algorithm to calculate control parameters for the next operation of the control system based on the overall evaluation value calculated by the comprehensive evaluation unit, and the condition setting unit sets the at least one operating condition and the control parameter calculated by the optimization unit in the control system.
• a parameter adjustment method is a parameter adjustment method executed by a computer that searches for optimal control parameters for operating a control system under multiple operating conditions by operating the control system while adjusting control parameters.
• the parameter adjustment method includes: a condition setting step of setting at least one of the multiple operating conditions and the control parameters in the control system; an evaluation step of calculating an evaluation value related to the operation of the control system when the control system operates under the at least one operating condition and the control parameters set in the condition setting step; a comprehensive evaluation step of calculating, as an overall evaluation value, a weighted average obtained using at least the maximum and minimum values of the evaluation values calculated in the evaluation step; and an optimization step of calculating, using an optimization algorithm, control parameters for the next operation of the control system based on the overall evaluation value calculated in the comprehensive evaluation step.
• the condition setting step the at least one operating condition and the control parameters calculated in the optimization step are set in the control system.
• a program according to one embodiment of the present disclosure causes the computer to execute the parameter adjustment method described above.
• This disclosure provides a parameter adjustment device, parameter adjustment method, and program that can reduce the time required for performance evaluation using simulation or an actual device when automatically adjusting control parameters for multiple operating conditions.
• FIG. 1 is a block diagram showing the configuration of a system including a parameter adjustment device according to the present disclosure.
• FIG. 2 is a detailed configuration diagram of the control system shown in FIG.
• FIG. 3 is a flowchart showing the operation of the parameter adjustment device according to this embodiment.
• FIG. 4 is a flowchart showing the detailed operation of step S205 shown in FIG.
• FIG. 5A is a diagram illustrating an example of an objective function corresponding to a plurality of operating conditions.
• FIG. 5B is a diagram showing the fluctuations of the control input and the controlled variable with respect to time change at points corresponding to the crosses a to i on the objective function in FIG. 5A.
• Figure 6 is a diagram showing an example of the transition of the selection probability of an operating condition and the overall evaluation value when the selection probability update unit increases the selection probability of the operating condition corresponding to the maximum evaluation value, and the overall evaluation unit uses the maximum value among the evaluation values obtained by adjustment as the overall evaluation value.
• Figure 7 shows an example of the transition of the selection probability of an operating condition and the overall evaluation value when the selection probability update unit increases the selection probability of the operating condition corresponding to the evaluation values that are the maximum and minimum values, and the overall evaluation unit sets the average value of the maximum and minimum values of the evaluation values obtained by adjustment as the overall evaluation value.
• FIG. 8 is a diagram showing an example of evaluation costs set for each operating condition.
• FIG. 8 is a diagram showing an example of evaluation costs set for each operating condition.
• FIG. 9 is a diagram showing an example of the transition of the selection probability of the operating condition and the overall evaluation value when the selection probability update unit further performs the processing of step S255 in FIG. 4 in order to reduce the total evaluation cost in the search.
• FIG. 10 is a diagram showing the relationship between the cumulative evaluation cost for each parameter adjustment method and the maximum value of the obtained evaluation value.
• FIG. 11 is a diagram showing an example of an image that the input/output unit displays on the input/output device.
• FIG. 12 is a table showing an example of success or failure of estimation of a plurality of operating conditions in a certain number of search trials.
• FIG. 13 is a diagram showing the relationship between the search progress and the correction coefficient.
• FIG. 14 is a diagram showing an example of the transition of the selection probability of the operating condition and the overall evaluation value when the process of step S255 in FIG. 4 is performed.
• FIG. 15 is a diagram showing the transition of the evaluation value when focusing on the operating conditions cond2 and cond3.
• FIG. 16 is a diagram showing a modification of the image shown in FIG.
• FIG. 1 is a block diagram showing the configuration of a system including a parameter adjustment device 1 according to the present disclosure.
• the present disclosure is composed of the parameter adjustment device 1, a control system 2 that is the target of adjustment by the parameter adjustment device 1, and an input/output device 3 that is a device through which a user makes various settings for the parameter adjustment device 1 and checks information on the results of the adjustment of control parameters.
• the parameter adjustment device 1 is a device that searches for optimal control parameters for operating the control system 2 under multiple operating conditions by operating the control system 2 while adjusting control parameters.
• the parameter adjustment device 1 is, for example, a terminal device such as a personal computer.
• the parameter adjustment device 1 also has, as its functional configuration, an input/output unit 11 and a control unit 12.
• the input/output unit 11 is an interface that communicates with the input/output device 3.
• the input/output unit 11 receives information about setting conditions for adjusting control parameters from the input/output device 3 and outputs the information to the control unit 12.
• the information about setting conditions for adjusting control parameters includes operating conditions used to search for control parameters, evaluation indices that are used by the evaluation unit 122 (described below) to calculate evaluation values, and a combination of operating conditions and evaluation costs for each operating condition.
• the evaluation indices are indices related to the performance of the control system 2 (e.g., the settling time and position deviation of the control system 2).
• the settling time is the time required for the control system 2 to move from the drive start position to an allowable position at which it can be evaluated as having reached the target position.
• the position deviation is the distance between the position of the control system 2 and the target position.
• the evaluation cost is the cost incurred when evaluating one operating condition, and corresponds to, for example, the time required for the evaluation, the energy and cost required for the evaluation, and the burden on the operator and the object to be adjusted (control system 2).
• the input/output unit 11 receives information about the results of the control parameter adjustment from the control unit 12 and outputs that information to the input/output device 3. A detailed explanation of the results of the control parameter adjustment will be provided later.
• the control unit 12 is realized by a microcomputer or processor, etc.
• the functions of the control unit 12 are realized by the microcomputer or processor, etc., executing a program stored in memory.
• control unit 12 has, as its functional configuration, a condition setting unit 121, an evaluation unit 122, a comprehensive evaluation unit 123, an optimization unit 124, a selection probability update unit 125, and an operating condition selection unit 126.
• the condition setting unit 121 acquires the operating conditions received by the input/output unit 11.
• the condition setting unit 121 sets at least one of the acquired operating conditions and control parameters in the control system 2. Specifically, the condition setting unit 121 sets the control parameters output by the optimization unit 124 in the control system 2.
• the condition setting unit 121 also sequentially provides signals based on the operating conditions included in the set of operating conditions output by the operating condition selection unit 126 to the control system 2 (i.e., sets them sequentially). Furthermore, providing signals based on operating conditions to the control system 2 sequentially means that after the operation of the control system 2 based on one operating condition is completed, another operating condition is set.
• the condition setting unit 121 outputs the operating conditions and control parameters set in the control system 2 to the evaluation unit 122.
• the condition setting unit 121 also outputs a set of operating conditions set in the control system 2 for each search to the input/output unit 11.
• the evaluation unit 122 observes the operational control of the control system 2 based on the operational conditions provided to the control system 2 by the condition setting unit 121.
• the evaluation unit 122 calculates an evaluation value for the observed operational control in accordance with the evaluation index accepted by the input/output unit 11.
• the evaluation unit 122 outputs a combination of the operational conditions and the evaluation value for each operational condition to the overall evaluation unit 123 and the selection probability update unit 125.
• the evaluation unit 122 also outputs the evaluation value of the control parameter obtained under each operational condition for each search trial to the input/output unit 11.
• the overall evaluation unit 123 calculates an overall evaluation value from the combination of the operating conditions output by the evaluation unit 122 and the evaluation value for each operating condition, and outputs this to the optimization unit 124.
• the overall evaluation unit 123 also outputs the overall evaluation value output to the optimization unit 124 to the input/output unit 11. A detailed explanation of the method for calculating the overall evaluation value will be given later.
• the optimization unit 124 uses a black-box optimization algorithm based on the overall evaluation value output by the overall evaluation unit 123 and outputs control parameters to the condition setting unit 121, with the intention of searching for control parameters that will result in the smallest overall evaluation value (i.e., the best value).
• the optimization unit 124 also outputs the control parameters for each search trial output to the condition setting unit 121 to the input/output unit 11.
• the black-box optimization algorithm is a well-known algorithm such as evolutionary computation or Bayesian optimization.
• the selection probability update unit 125 updates the selection probability table and cost-gradient selection probability table for operating conditions stored internally based on the combination of operating conditions and evaluation values for each operating condition output by the evaluation unit 122 and the combination of operating conditions and evaluation costs for each operating condition received by the input/output unit 11.
• the selection probability update unit 125 outputs at least one of the updated selection probability table and cost-gradient selection probability table to the operating condition selection unit 126.
• the selection probability update unit 125 also outputs the selection probability table or cost-gradient selection probability table output to the operating condition selection unit 126 to the input/output unit 11.
• the selection probability table is a table that indicates the probability of selection by the operating condition selection unit 126 for each operating condition.
• the cost-gradient selection probability table is a table in which the probability for each operating condition contained in the selection probability table is weighted according to the evaluation cost for each operating condition. A detailed explanation of the selection probability table and cost-gradient selection probability table will be given later.
• the operating condition selection unit 126 uses the selection probability table or cost-graded selection probability table output by the selection probability update unit 125 to probabilistically select at least one operating condition from all operating conditions received by the input/output unit 11.
• the operating condition selection unit 126 outputs the selected operating conditions to the condition setting unit 121 as an operating condition set.
• Information obtained from the components of the parameter adjustment device 1 is stored within the input/output unit 11, and is displayed on the input/output device 3 after undergoing simple information processing as appropriate.
• the input/output device 3 is a device that inputs and outputs data to and from the parameter adjustment device 1, and is, for example, a terminal device such as a computer, tablet terminal, or smartphone. Communication between the input/output device 3 and the input/output unit 11 may be wired or wireless. The parameter adjustment device 1 and the input/output device 3 may be included in the same terminal device.
• FIG. 2 is a detailed configuration diagram of the control system 2 shown in FIG.
• control system 2 is composed of a control unit 21 and a controlled object 22, such as a power supply device.
• the control system 2 controls operation based on control parameters set by the condition setting unit 121 and signals based on operating conditions provided by the condition setting unit 121.
• the signal based on the operating conditions may be, for example, a target signal given to the control unit 21 or an operating mode that switches the behavior of the control unit 21, or a disturbance signal given to the controlled object 22.
• the target signal is, for example, a signal that includes information such as the target voltage output by the control system 2.
• the operating mode is, for example, a signal that switches the control state of the control system 2, such as a signal that switches the control state of the power supply device to either constant voltage control, constant current control, or constant power control.
• the disturbance signal is, for example, a signal that includes information such as input voltage fluctuations or load current fluctuations given to the power supply device.
• the control unit 21 is realized by a microcomputer, a processor, or the like.
• the functions of the control unit 21 are realized by the microcomputer, processor, or the like executing a program stored in memory.
• the control unit 21 receives control parameters, a target signal, and an operating mode from the condition setting unit 121.
• the control unit 21 also outputs a control input signal, which is a signal that controls the operation of the controlled object 22, to the controlled object 22.
• the control parameters are, for example, proportional gain, integral gain, and differential gain used in PID (Proportional-Integral-Differential) control.
• the controlled object 22 is an object controlled by the control unit 21, such as a power supply circuit, a battery, or a load.
• the controlled object 22 also outputs an internal signal to the evaluation unit 122.
• the internal signal may be, for example, a signal containing information about the control amount with which the control unit 21 controls the controlled object 22, or the value of a signal that the control unit 21 cannot directly control or observe.
• the internal signal may also be a control input signal that the control unit 21 provides to the controlled object 22.
• the internal signal is a signal containing information such as the output voltage or inductor current.
• FIG. 3 is a flowchart showing the operation of the parameter adjustment node 1 according to this embodiment.
• step S201 the user inputs operating conditions, evaluation indices, and combinations of operating conditions and evaluation costs for each operating condition to the input/output device 3.
• the input/output unit 11 accepts the information input by the user and outputs the operating conditions to the condition setting unit 121, the evaluation indices to the evaluation unit 122, and combinations of operating conditions and evaluation costs for each operating condition to the selection probability update unit 125.
• the operating conditions may then be managed uniformly within the parameter adjustment device 1, for example, as a combination of an operating condition number and a signal to be provided to the control system 2.
• step S202 the selection probability update unit 125 initializes the search progress, the selection probability table, and the cost gradient selection probability table.
• the search progress degree (hereinafter also referred to as the search progress degree) is a scalar value held by the selection probability update unit 125, and represents the degree of progress in the control parameter search performed by the parameter adjustment device 1.
• the search progress degree takes on values between 0 and 1. Note that the selection probability update unit 125 sets the search progress degree to 0 during initialization in step S202.
• the selection probability table is a table of table values held by the selection probability update unit 125, and is a table showing the probability that the operating condition selection unit 126 will select each operating condition for all operating conditions output by the input/output unit 11 to the selection probability update unit 125. Note that the selection probability update unit 125 sets all of these table values to 1 during initialization in step S202.
• the cost-gradient selection probability table is a table value held by the selection probability update unit 125, and is a correction of the probability of each operating condition set in the selection probability table based on the cost of each operating condition, increasing the probability if the cost is greater than a certain standard and decreasing the probability if the cost is less than a certain standard. Note that the selection probability update unit 125 sets all of these table values to 1 during initialization in step S202.
• step S203 the condition setting unit 121 sets the control parameters in the control system 2 and sequentially provides all of the acquired operating conditions to the control system 2.
• the control parameters set by the condition setting unit 121 may be predetermined initial values. Then, the control system 2 sequentially executes control based on the operating conditions provided by the condition setting unit 121 and the set control parameters.
• step S204 the evaluation unit 122 observes the operating waveforms under each operating condition from the control system 2 and calculates an evaluation value for each operating condition based on the acquired evaluation index. The evaluation unit 122 then outputs a combination of the operating condition and the evaluation value for each operating condition to the selection probability update unit 125.
• the parameter adjustment device 1 may also perform step S209 or step S210, which will be described later.
• step S205 the selection probability update unit 125 updates the selection probability table and the cost gradient selection probability table according to the combination of operating conditions and evaluation values obtained in step S204 or step S208 described below.
• step S206 the operating condition selection unit 126 selects an operating condition with a probability based on the value of the cost-graded selection probability table updated in step S205, creates an operating condition set, and outputs it to the condition setting unit 121.
• the operating condition selection unit 126 creates an operating condition set that includes at least one operating condition. If the operating condition selection unit 126 creates an operating condition set that does not include an operating condition by selecting an operating condition based on the cost-graded selection probability table, it creates a new operating condition set by, for example, selecting the operating condition with the highest probability in the cost-graded selection probability table.
• step S207 the condition setting unit 121 sequentially provides the control system 2 with the operating conditions included in the operating condition set output by the operating condition selection unit 126 in step S206. Then, the control system 2 sequentially executes control based on the operating conditions provided by the condition setting unit 121 and the set control parameters.
• step S208 the evaluation unit 122 observes the operating waveforms under each operating condition from the control system 2 and calculates an evaluation value for each operating condition based on the acquired evaluation index. The evaluation unit 122 then outputs a combination of the operating condition and the evaluation value for each operating condition to the overall evaluation unit 123.
• step S209 the overall evaluation unit 123 obtains the maximum and minimum values of each evaluation value calculated by the evaluation unit 122 in step S208, and calculates the average of the maximum and minimum values as the overall evaluation value.
• calculating the average of the maximum and minimum values as the overall evaluation value is one example of a method for calculating the overall evaluation value, and the overall evaluation value may also be an appropriate weighted average of the maximum and minimum values.
• the overall evaluation value may also be a weighted average of the top multiple evaluation values including the maximum value, and instead of the minimum value, the overall evaluation value may also be a weighted average of the bottom multiple evaluation values including the minimum value.
• step S210 the optimization unit 124 calculates the next control parameters to be tried based on the black-box optimization algorithm, using the overall evaluation value calculated by the overall evaluation unit 123 in step S209 as the objective function value to be minimized, and outputs the calculated control parameters to the condition setting unit 121.
• step S211 the condition setting unit 121 determines whether the search operation for the control parameters defined in steps S205 to S210 has been performed a certain number of times. If the condition setting unit 121 determines that the search operation has not been performed a certain number of times (No in step S211), the flowchart proceeds to step S205. On the other hand, if the condition setting unit 121 determines that the search operation has been performed a certain number of times (Yes in step S211), the flowchart proceeds to step S212.
• step S212 the input/output unit 11 outputs to the input/output device 3 the best parameters calculated by the evaluation unit 122 based on the evaluation index for each operating condition selected in each search operation.
• the best parameters are defined as the control parameters that give the smallest (best) value among the history of the largest (worst) evaluation values for the operating conditions selected in each search operation.
• the selection probability update unit 125 may update the selection probabilities set in the selection probability table and the cost-graded selection probability table so that they are the same value, without applying a cost gradient (i.e., a weight according to the evaluation cost) in step S205.
• a cost gradient i.e., a weight according to the evaluation cost
• the selection probability update unit 125 may update the selection probability table in step S205, and the operation condition selection unit 126 may create and output a set of operation conditions based on the selection probability table in step S206.
• the overall evaluation unit 123 calculates the weighted average of the maximum and minimum values of multiple evaluation values as the overall evaluation value, making it possible to obtain a variety of overall evaluation values in each search operation. This allows the optimization unit 124 to use the overall evaluation value obtained in each search operation as a hint to calculate the next control parameter to be tried.
• the overall evaluation unit 123 calculates the overall evaluation value as a weighted average of multiple evaluation values, including the maximum and minimum evaluation values, making it possible to obtain a wider variety of overall evaluation values for each search operation.
• FIG. 4 is a flowchart showing the detailed operation of step S205 shown in Figure 3.
• the selection probability update unit 125 identifies the operating condition with the maximum evaluation value from the combination of the operating conditions and the evaluation values for each operating condition obtained in step S204 or step S208, and updates the selection probability table so that the selection probability of that operating condition is higher than the selection probabilities of other operating conditions. Specifically, if the selection probability of the operating condition with the maximum evaluation value is ps, the selection probability update unit 125 increases the selection probability ps to a higher value than the selection probabilities of other operating conditions, updating the selection probability ps using an appropriate positive value cp according to the following equation 1.
• the selection probability update unit 125 identifies the operating condition with the smallest evaluation value from the combination of the operating conditions and the evaluation values for each operating condition obtained in step S204 or step S208, and updates the selection probability table so that the selection probability of that operating condition increases or decreases according to the search progress. Specifically, if the selection probability of the operating condition with the smallest evaluation value is ps, the selection probability update unit 125 updates it using appropriate positive values cp and cn and the search progress ⁇ as shown in Equation 2 below. Note that Equation 2 below increases the selection probability of the operating condition when the search progress ⁇ is smaller than a threshold, and decreases the selection probability of the operating condition when the search progress ⁇ is larger than the threshold. Furthermore, Equation 2 below sets the selection probability ps of the operating condition when the search progress ⁇ is larger than the threshold to be lower than the selection probability ps of the operating condition when the search progress ⁇ is smaller than the threshold.
• the selection probability update unit 125 updates the selection probability table based on the combination of the operating conditions and the evaluation values for each operating condition obtained in step S204 or step S208, so as to reduce the selection probability of operating conditions whose evaluation value is neither the maximum nor minimum value. Specifically, if the selection probability of an operating condition whose evaluation value is neither the minimum nor the maximum value is ps, the selection probability update unit 125 updates the selection probability ps using an appropriate positive value cn according to the following equation 3 so that the selection probability ps decreases.
• the selection probability update unit 125 may also reverse the order of the processes in steps S251, S252, and S253.
• the updated selection probabilities in steps S251, S252, and S253 are values ranging from the minimum probability ⁇ (described below) to 1. Furthermore, the threshold value in step S252 is a value ranging from the minimum probability ⁇ (described below) to 1.
• step S254 the selection probability update unit 125 updates the search progress. Specifically, the selection probability update unit 125 updates the search progress ⁇ using the update step ⁇ according to the following equation 4.
• step S255 the selection probability update unit 125 multiplies the probability for each operating condition in the selection probability table by a coefficient corresponding to the evaluation cost for that operating condition, and updates the cost-gradient selection probability table. Specifically, the selection probability update unit 125 sets the cost-gradient selection probability psc as shown in Equation 5 below, where ps is the selection probability for an operating condition stored in the selection probability table, C is the evaluation cost for that operating condition, and Cmin is the lowest evaluation cost among all operating conditions.
• the selection probability update unit 125 may adjust the magnitude of the cost-based correction coefficient Cmin/C according to the search progress level ⁇ . For example, if the search progress level ⁇ exceeds a threshold, the selection probability update unit 125 may set the correction coefficient Cmin/C to 1.
• step S256 the selection probability update unit 125 constrains the selection probability of the operating conditions held in the selection probability table and the cost-graded selection probability table to be between the minimum probability ⁇ and 1.
• the minimum probability ⁇ is a value that prevents the selection probability from becoming completely 0, and allows for a slight possibility that the operating condition will be selected again.
• the parameter adjustment device 1 performs search operations using the operating conditions selected by the operating condition selection unit 126, thereby reducing the number of evaluations required for each search operation.
• the operating condition selection unit 126 can select a variety of operating conditions.
• the parameter adjustment device 1 performs each search operation using a variety of operating conditions, and the overall evaluation unit 123 can obtain a variety of overall evaluation values.
• the operating condition selection unit 126 is more likely to select the operating condition with the smallest value, so the parameter adjustment device 1 will often perform search operations that include that operating condition. This allows the optimization unit 124 to calculate the next control parameter to try using the comprehensive evaluation value of various values as a hint in the early stages of the search. Furthermore, from the middle stage of the search onwards, the operating condition selection unit 126 is less likely to select the operating condition with the smallest value, so the parameter adjustment device 1 will less often perform search operations that include that operating condition. This allows the parameter adjustment device 1 to reduce the number of evaluations required for each search operation.
• the operating condition selection unit 126 is more likely to select operating conditions with low evaluation costs and less likely to select operating conditions with high evaluation costs, allowing the parameter adjustment device 1 to reduce the evaluation costs associated with each search operation.
• the operating conditions selected by the operating condition selection unit 126 change depending on the progress of the search, allowing the parameter adjustment device 1 to use a variety of operating conditions in the search.
• Fig. 5A is a diagram showing an example of an objective function corresponding to a plurality of operating conditions.
• Fig. 5B is a diagram showing the fluctuations of the control input and controlled variable over time at points corresponding to the crosses a to i on the objective function in Fig. 5A.
• FIG. 5A is a diagram showing an example of an objective function corresponding to multiple operating conditions cond0 to cond7.
• the objective function is a function that represents the correspondence between the control parameters on the horizontal axis and their evaluation values on the vertical axis.
• the control parameters shown on the horizontal axis are the control parameters set in the control system 2 by the condition setting unit 121, and the evaluation values shown on the vertical axis are the evaluation values calculated by the evaluation unit 122.
• FIG. 5A shows the objective function obtained when the parameter adjustment device 1 adjusts two types of control parameters while fixing one of the control parameters.
• the objective functions corresponding to operating conditions cond0 and cond1 are objective functions with a minimum evaluation value of approximately 1.
• the objective functions corresponding to operating conditions cond4, cond5, cond6, and cond7 are objective functions with a minimum evaluation value of approximately 3.7.
• the objective functions corresponding to operating conditions cond2 and cond3 are objective functions with a minimum evaluation value of approximately 5. Furthermore, among the objective functions with a minimum evaluation value of approximately 1, the objective function for which the control parameter value when the minimum evaluation value is obtained is approximately 0 corresponds to operating condition cond0, and the objective function for which the control parameter value when the minimum evaluation value is obtained is approximately 0.3 corresponds to operating condition cond1.
• the objective function whose control parameter value when the minimum evaluation value is obtained is approximately -0.3 corresponds to operating condition cond7.
• the objective function whose control parameter value when the minimum evaluation value is obtained is approximately 0 corresponds to operating condition cond6.
• the objective function whose control parameter value when the minimum evaluation value is obtained is approximately 0.3 corresponds to operating condition cond5.
• the objective function whose control parameter value when the minimum evaluation value is obtained is approximately 0.9 corresponds to operating condition cond4.
• the objective function whose control parameter value when the minimum evaluation value is obtained is a negative value corresponds to operating condition cond2.
• the objective function whose control parameter value when the minimum evaluation value is obtained is a positive value corresponds to operating condition cond3. Furthermore, the objective function max indicated by the dashed line is the objective function drawn with the largest evaluation value obtained for each control parameter from the objective functions corresponding to multiple operating conditions cond0 to cond7.
• the crosses a, b, and c are marks attached to operating condition cond3.
• the crosses d, e, and f are marks attached to operating condition cond7.
• the crosses g, h, and i are marks attached to operating condition cond1. Furthermore, the crosses a, d, and g are points where the control parameter value is 1, the crosses b, e, and h are points where the control parameter value is 2, and the crosses c, f, and i are points where the control parameter value is 3.
• control inputs and controlled variables corresponding to the crosses a to i marked on the objective function in Figure 5A show the control inputs and controlled variables corresponding to the crosses a to i marked on the objective function in Figure 5A.
• the control input is a signal output by the control unit 21 to the controlled object 22, and in the example in Figure 5B, it is set to saturate at a minimum value of -1 and a maximum value of 1.
• the controlled variable is the output value of the controlled object 22 that can be observed by the evaluation unit 122, and in the example in Figure 5B, the control unit 21 controls the output value with a target value of 1.
• the evaluation unit 122 calculates the integral value of the deviation between the controlled variable and the target value as the evaluation value. Specifically, the evaluation unit 122 calculates the total area enclosed by the waveform indicating the controlled variable and the straight line indicating the target value over a certain period of time as the evaluation value.
• FIG. 5B show the evaluation value and response for operating condition cond3.
• the waveform of the control input repeatedly saturates for all control parameters, and the movement of the controlled variable is oscillatory. Due to these characteristics, the objective function for operating condition cond3 shown in Figure 5A has the maximum evaluation value for most control parameters. In this objective function, the control parameters that improve response and reduce the evaluation value are distributed over a very narrow range. Note that operating condition cond2 has similar characteristics and objective function shape to operating condition cond3.
• (d), (e), and (f) in Figure 5B show the evaluation value and response for operating condition cond7.
• the control input waveform repeatedly saturates, resulting in oscillatory behavior of the controlled variable.
• the control input waveform is not constantly saturated, resulting in improved response.
• the amplitude of the controlled variable in (d) in Figure 5B is smaller than that in (e) and (f) in Figure 5B.
• the amplitude of the controlled variable in (e) and (f) in Figure 5B is also smaller than that in (b) and (c) in Figure 5B when the same control parameters are set.
• the objective function for cond7 shown in Figure 5A has a narrower flat section and a wider valley section than those for operating conditions cond2 and cond3. Furthermore, the evaluation values obtained for most control parameters are smaller than those obtained for operating conditions cond2 and cond3. Note that operating conditions cond4, cond5, and cond6 also have similar properties and objective function shapes to operating condition cond7.
• (g), (h), and (i) in Figure 5B show the evaluation value and response for operating condition cond1.
• the control input waveform repeatedly saturates, resulting in oscillatory behavior of the controlled variable.
• the control input waveform is not constantly saturated, resulting in improved response.
• the amplitude of the controlled variable in (g) and (h) in Figure 5B is smaller than that in (i) in Figure 5B.
• the amplitude of the controlled variable in (i) in Figure 5B is also smaller than that in (f) in Figure 5B when the same control parameters are set.
• the objective function for cond1 shown in Figure 5A has a narrower flat zone and wider valley zones than those for operating conditions cond4 to cond7. Furthermore, for most control parameters, the evaluation value is smaller than those for operating conditions cond4 to cond7. Note that operating condition cond0 also has similar properties and objective function shape to operating condition cond1.
• the dotted line shown in Figure 5A is the objective function that takes the maximum value among the objective functions for all operating conditions, and is the objective function that the parameter adjustment device 1 ultimately wants to minimize.
• the objective function drawn by the dotted line can also be considered as the evaluation value of the operating condition that is the bottleneck for a certain control parameter.
• operating conditions cond2 and cond3 are the maximum values related to the operating conditions, so simply thinking about it, if the parameter adjustment device 1 performs control execution, evaluation, and optimization only for operating conditions cond2 and cond3, this is equivalent to performing control execution, evaluation, and optimization for the maximum values.
• FIG. 6A shows an example of the transitions in the selection probability of an operating condition and the overall evaluation value when the selection probability update unit 125 increases the selection probability of the operating condition corresponding to the maximum evaluation value, and the overall evaluation unit 123 sets the maximum of the evaluation values obtained by adjustment as the overall evaluation value.
• (a) of Figure 6A shows a table showing the selection probability of an operating condition versus the trial representing the number of search trials for the control parameters. The selection probability for each trial is the selection probability set in the cost-graded selection probability table for each search trial.
• (b) on the left side of Figure 6A shows a table showing the transitions in the evaluation value for each operating condition versus the trial representing the number of search trials for the control parameters.
• (b) on the right side of Figure 6A shows the evaluation values displayed using a color bar.
• FIG. 6 is an explanatory diagram of the operation of the parameter adjustment device 1 when the processing that achieves the effects of the present disclosure is omitted or modified. That is, the operation performed by the parameter adjustment device 1 is the same as when the processing of steps S252 and S254 in FIG. 4 is omitted, the selection probability of the operating condition that takes the minimum value in step S253 in FIG. 4 is also lowered, the maximum value rather than the average value is used as the overall evaluation value in step S209 in FIG. 3, and the cost gradient is not applied in step S255 in FIG. 4, and the selection probabilities set in the selection probability table and the cost-graded selection probability table are changed to the same value.
• the selection probabilities for all operating conditions are close to 1 at the beginning of the search. This is because in step S202 of Figure 3, the selection probability update unit 125 initializes the selection probabilities set in the selection probability table and the cost-graded selection probability table to 1. As a result, as shown in Figure 6(b), at the beginning of the search, the parameter adjustment device 1 selects and evaluates almost all operating conditions.
• the selection probability update unit 125 performs the processes of steps S251 and S253 of Figure 4 (however, it also lowers the selection probability of the operating condition with the smallest value).
• the selection probability update unit 125 updates the selection probability table and the cost-gradient selection probability table so that the selection probability of operating condition cond2 or cond3 becomes higher and the selection probability of other operating conditions becomes lower.
• the selection probability is constrained to be between the minimum probability ⁇ and 1 by the process of step S256, it will never take a value greater than 1 or less than the minimum probability ⁇ .
• the operating condition selection unit 126 selects operating conditions based on the values of the selection probability table and cost-gradient selection probability table updated in this way (i.e., the processing of step S206 in Figure 3 is performed). As a result, as shown in Figure 6(b), the frequency with which operating conditions other than operating conditions cond2 and cond3 are evaluated decreases as the search progresses. As a result, the parameter adjustment device 1 performs control execution, evaluation, and optimization only for operating conditions that are bottlenecks and are likely to produce the overall evaluation value (i.e., the maximum value) shown in Figure 6(c).
• FIG. 7 is a diagram illustrating an example of how the selection probability of an operating condition and the overall evaluation value change over time when the selection probability update unit 125 increases the selection probability of an operating condition corresponding to the maximum and minimum evaluation values, and the overall evaluation unit 123 sets the average of the maximum and minimum evaluation values obtained by adjustment as the overall evaluation value.
• FIG. 7 is an explanatory diagram illustrating an example of the operation of the parameter adjustment device 1 for the objective function of multiple operating conditions shown in FIGS. 5A and 5B . Note that the parameter adjustment device 1 described in FIG. 7 differs from the parameter adjustment device 1 described in FIG.
• step 6 in that it adds the processes of steps S252 and S254 of FIG. 4 , does not lower the selection probability of an operating condition with the minimum value in step S253 of FIG. 4 , and instead sets the average value as the overall evaluation value in step S209 of FIG. 3 .
• FIG. 7 is a diagram showing the same content as (a) and (b) of Figure 6, respectively.
• (c) of Figure 7 is a diagram showing a color bar indicating the transition of only the maximum value of the evaluation values for each operating condition in each trial shown in (b) of Figure 7.
• (d) of Figure 7 is a diagram showing the transition of the selection probability table and the transition of the search progress updated in step S254 of Figure 4 for the trial representing the number of search trials for the control parameters.
• (e) of Figure 7 is a diagram showing the transition of the selected operating condition and its evaluation value, as well as the transition of the overall evaluation value (the average of the maximum and minimum values in Figure 7) calculated in step S209 for the trial representing the number of search trials for the control parameters.
• the selection probability update unit 125 performs the processing of step S252 in Figure 4.
• the selection probability update unit 125 updates the selection probabilities of not only operating conditions cond2 and cond3 but also operating conditions cond0 and con1 so that they are higher.
• the selection probabilities of operating conditions cond0 to cond3 remain near 1, and the selection probabilities of the other operating conditions are lower.
• this parameter adjustment method prevents the search from stagnating due to only obtaining a certain evaluation value in the early stages of the search, and subsequently narrows down to the operating conditions that are bottlenecks, allowing for control execution, evaluation, and optimization.
• the overall evaluation unit 123 calculates the weighted average of the maximum and minimum values of multiple evaluation values as the overall evaluation value, making it possible to obtain a variety of overall evaluation values in each search operation. This allows the optimization unit 124 to use the overall evaluation value obtained in each search operation as a hint to calculate the next control parameter to be tried. This allows the parameter adjustment device 1 to avoid adjustment stagnation in the early stages of search.
• the operating condition selection unit 126 is more likely to select the operating condition with the smallest value, so the parameter adjustment device 1 will often perform search operations that include that operating condition. This allows the optimization unit 124 to calculate the next control parameter to be tried using the comprehensive evaluation value of various values as a hint in the early stages of the search, allowing the parameter adjustment device 1 to avoid adjustment stagnation. Furthermore, in the middle and later stages of the search, the operating condition selection unit 126 is less likely to select the operating condition with the smallest value, so the parameter adjustment device 1 will less often perform search operations that include that operating condition. This allows the parameter adjustment device 1 to reduce the number of evaluations required for each search operation.
• the total evaluation cost required for adjusting the control parameters may increase depending on the evaluation cost for each operating condition. Specifically, since the evaluation cost for each operating condition is not necessarily the same, actively selecting operating conditions with high evaluation costs may result in a problem of increasing the total evaluation cost.
• the parameter adjustment device 1 is required to minimize the overall evaluation value with as little evaluation cost as possible.
• Figure 8 is a diagram showing an example of the evaluation costs set for each operating condition.
• Figure 9 is a diagram showing an example of the transitions in the selection probability and overall evaluation value of operating conditions when the selection probability update unit 125 further performs the processing of step S255 in Figure 4 to reduce the total evaluation cost in the search.
• the evaluation cost for each operating condition shown in Figure 9 is the evaluation cost shown in Figure 8.
• Figure 9 is also an explanatory diagram showing an example of the operation of the parameter adjustment device 1 for the objective functions of the multiple operating conditions shown in Figures 5A and 5B. Note that (a), (b), (c), (d), and (e) in Figure 9 are diagrams showing the same content as (a), (b), (c), (d), and (e) in Figure 7, respectively.
• Evaluating operating conditions incurs evaluation costs as shown in Figure 8. Specifically, the evaluation cost for even-numbered operating conditions is 1, and the evaluation cost for other operating conditions is 2.
• the selection probability of odd-numbered operating conditions shown in Figure 9(a) fluctuates within the range of 0 to 0.5 throughout the entire search.
• the cost-gradient selection probability table updated based on Equation 5 above in step S255 of Figure 4 is reflected.
• the selection probability of operating condition cond3 is lower than the selection probability of operating condition cond2, which has a similar objective function.
• the selection probability of operating condition cond1 is lower than the selection probability of operating condition cond0, which has a similar objective function.
• the selection probability of operating conditions cond5 and cond7 is lower than the selection probability of operating conditions cond4 and cond6, which have similar objective functions.
• the evaluation value shown in Figure 9(c) improves from the beginning of the search, and, similar to the evaluation value shown in Figure 7(c), it prevents stagnation in the early stages of the search.
• this parameter adjustment method can reduce the total evaluation cost in the search while avoiding the evaluation of operating conditions that have a high evaluation cost.
• the operating condition selection unit 126 is more likely to select operating conditions with low evaluation costs and less likely to select operating conditions with high evaluation costs, allowing the parameter adjustment device 1 to reduce the evaluation costs associated with each search operation.
• the selection probability update unit 125 may correct the evaluation cost according to the search progress level ⁇ . For example, in the early stages of the search, the selection probability update unit 125 updates the cost-gradient selection probability table based on the above formula 5 to avoid evaluating operating conditions with high evaluation costs. On the other hand, in the later stages of the search, the selection probability update unit 125 corrects the evaluation cost to improve the accuracy of the control parameters, and updates the cost-gradient selection probability table so that the operating condition selection unit 126 can select many operating conditions that are important for adjusting the control parameters, regardless of the evaluation cost.
• FIG. 10 is a diagram showing the relationship between the cumulative evaluation cost required for each parameter adjustment method and the maximum value of the obtained evaluation values.
• FIG. 10 is a diagram showing a table in which the horizontal axis represents the cumulative evaluation cost and the vertical axis represents the maximum value of the obtained evaluation values.
• the cumulative evaluation cost is a numerical value obtained by adding up all the evaluation costs required for each search operation.
• the parameter adjustment method that obtains the dotted line is an adjustment method that always controls and executes all operating conditions in the search operation, and evaluates the maximum of the obtained evaluation values as the overall evaluation value.
• the dashed line shows the relationship obtained when a parameter adjustment method that evaluates the average of the maximum and minimum of the obtained evaluation values as the overall evaluation value in step S209 of FIG. 3 is used among the operations performed by the parameter adjustment method that obtains the dotted line.
• the parameter adjustment method that obtains the dashed line is an adjustment method that always controls and executes all operating conditions in the search operation, and evaluates the average of the maximum and minimum of the obtained evaluation values as the overall evaluation value.
• the average of the maximum and minimum of the obtained evaluation values is evaluated as the overall evaluation value.
• the maximum of the obtained evaluation values is evaluated as the overall evaluation value. For example, if the parameter adjustment device 1 adjusts the control parameters with the goal of reducing the maximum of the obtained evaluation values to 6 or less, a parameter adjustment device 1 using one of the former three parameter adjustment methods will achieve this goal with a lower cumulative evaluation cost than a parameter adjustment device 1 using one of the latter two parameter adjustment methods.
• a parameter adjustment device 1 using the latter parameter adjustment method can reduce the number of evaluations required for each search operation compared to a parameter adjustment device 1 using the former parameter adjustment method. Therefore, a parameter adjustment device 1 using the latter parameter adjustment method can reduce the evaluation cost for each search operation compared to a parameter adjustment device 1 using the former parameter adjustment method.
• the parameter adjustment method when the two-dot-dash line is obtained the operating conditions selected based on a cost-graded selection probability table in which the selection probability of each operating condition set in the selection probability table is multiplied by a coefficient corresponding to the evaluation cost for each operating condition are controlled and executed in each search operation.
• the parameter adjustment method when the two-dot-dash line is obtained unlike the parameter adjustment method when the solid line is obtained, can reduce the total evaluation cost in the search while avoiding the evaluation of operating conditions with high evaluation costs. Therefore, a parameter adjustment device 1 using the former parameter adjustment method can reduce the evaluation cost for each search operation more than a parameter adjustment device 1 using the latter parameter adjustment method.
• FIG. 11 is a diagram showing an example of an image displayed on the input/output device 3 by the input/output unit 11.
• the image includes a file selection button 401, an operating condition setting area 402, an evaluation index setting area 403, a cost setting area 404, an adjustment execution button 405, an adjustment status display area 406, an adjustment result display area 407, a comprehensive evaluation value display area 408, a maximum evaluation value display area 409, a selection probability table display area 410, a selection probability table with cost gradient display area 411, and a best parameter display area 412.
• the image does not need to include all of the above-mentioned components, and may instead display only some of the components.
• the user can provide pre-set operating conditions to the input/output unit 11 via the file selection button 401.
• the input/output unit 11 accepts eight operating conditions by reading a settings file.
• the user can set the operating conditions that the input/output unit 11 outputs to the condition setting unit 121 via the operating condition setting area 402.
• the shape of the input signal that the condition setting unit 121 provides to the control system 2 for each of the eight operating conditions is set by reading a settings file.
• the user can set an evaluation index for the evaluation unit 122 to evaluate the operating waveform via the evaluation index setting area 403.
• the amount of overshoot when an input signal is applied using pull-down is set as the evaluation index.
• the user can set the evaluation cost for each operating condition set in the operating condition setting area 402 via the cost setting area 404.
• This setting can be set directly by the user using a bar graph as a slider UI, or it can be set automatically by the parameter adjustment device 1 based on the length of the input signal, etc.
• the user can use the adjustment execution button 405 to execute the adjustment operation of the parameter adjustment device 1 based on the operating conditions set in the operating condition setting area 402, the evaluation index set in the evaluation index setting area 403, and the evaluation cost set in the cost setting area 404.
• the adjustment status display area 406 displays in real time an image showing the control parameters set in the control unit 21 by the condition setting unit 121 and the operating conditions given to the control system 2.
• the horizontal axis represents each trial from the start of control parameter adjustment to the present
• the vertical axis represents the operating conditions
• an image is displayed showing the evaluation values calculated by the evaluation unit 122 for the operating conditions set by the condition setting unit 121 in a heat map.
• the evaluation values for operating conditions that the condition setting unit 121 did not set for the control system 2 in a certain trial are displayed as blanks.
• the horizontal axis represents each trial from the start of control parameter adjustment to the present, and an image is displayed showing the overall evaluation value calculated by the overall evaluation unit 123 in a heat map.
• the horizontal axis represents each trial from the start of control parameter adjustment to the present, and an image is displayed showing the maximum (worst) evaluation value for the operating conditions in each search trial in a heat map.
• each trial from the start of control parameter adjustment to the present is plotted on the horizontal axis, and an image is displayed showing the progression of the selection probability in the selection probability table and the selection probability in the cost gradient selection probability table held by the selection probability update unit 125 as a line graph.
• the best parameter display area 412 displays the evaluation value when the smallest value was obtained from the history of the largest evaluation values for the operating conditions selected in each search operation from the start of control parameter adjustment to the present, and the control parameter that produced that result.
• the operating condition setting area 402, cost setting area 404, and adjustment result display area 407 may be displayed so that the vertical positions of the operating conditions displayed in each UI diagram match.
• the parameter adjustment device 1 can output the optimal control parameters obtained through the search to the input/output device 3.
• the selection probability update unit 125 may update the search progress ⁇ according to the following equations 6 and 7 instead of the above equation 4.
• ⁇ k shown in equation 6 is the estimated pass rate for the number of search attempts k (k is a natural number)
• Np is the number of operating conditions that are estimated to pass when the number of search attempts k is reached
• N is the total number of operating conditions.
• max k is a symbol indicating the maximum value related to the number of search attempts k.
• the selection probability update unit 125 calculates the search progress ⁇ , which depends on the estimated pass rate ⁇ k , using the above equations 6 and 7.
• the calculation of the estimated pass rate will be explained using the specific example shown in FIG. 12.
• FIG. 12 is a table showing an example of the estimated pass/fail results for multiple operating conditions cond0 to 7 in a certain number of search trials k.
• the operating conditions actually given to the control system 2 are cond2, cond3, cond4, and cond6.
• the table shows the operating conditions, the selection probability of each operating condition in search attempt number k, the pass/fail status of the operating conditions controlled and executed in search attempt number k, and the estimated pass/fail status of each operating condition in search attempt number k.
• the pass/fail column shows whether the operating condition controlled and executed during search attempt count k passed or failed. For example, if the evaluation value obtained by controlling and executing a certain operating condition is below the reference value, the operating condition passed; if the evaluation value obtained by controlling and executing a certain operating condition is above the reference value, the operating condition failed.
• the estimated pass/fail column shows pass/fail results, including those for operating conditions for which no control was executed.
• the pass/fail result for an operating condition for which no control was executed in search attempt number k is considered to be the pass/fail result of the operating condition with the next lowest selection probability after the operating condition for which no control was executed, among the operating conditions for which control was executed in that search attempt number.
• the pass/fail result of cond7 is considered to be the pass/fail result (fail) of cond6, which has the next lowest selection probability after cond7
• the pass/fail results of cond0, cond1, and cond5 are considered to be the pass/fail result (pass) of cond4, which has the next lowest selection probability after cond0, cond1, and cond5.
• the estimated pass rate ⁇ k is as follows:
• the selection probability update unit 125 may set the cost-gradient selection probability psc as shown in the following equation 8 instead of the above equation 5. Note that ⁇ in equation 8 is a correction coefficient.
• Figure 13 shows the relationship between the search progress level ⁇ and the correction coefficient.
• (a) of Figure 13 shows the relationship between the search progress level ⁇ and the cost-based correction coefficient Cmin/C in operation example 2
• (b) of Figure 13 shows the relationship between the search progress level ⁇ and the correction coefficient ⁇ in a modified version of operation example 2.
• the cost-based correction coefficient Cmin/C shows a constant value regardless of the search progress level ⁇ . For example, when the evaluation cost is 1, the correction coefficient Cmin/C shows 1, and when the evaluation cost is 2, the correction coefficient Cmin/C shows 0.5.
• the selection probability update unit 125 sets the correction coefficient ⁇ to 1.
• the selection probability update unit 125 varies the correction coefficient ⁇ between 0 and 1 according to the search progress level ⁇ . In other words, for operating conditions with a non-minimum evaluation cost, the selection probability update unit 125 varies the correction coefficient ⁇ between 0 and 1 according to the search progress level ⁇ .
• the parameter adjustment device 1 sets the selection probability of operating conditions so that it is difficult to select operating conditions with high evaluation costs in the early stages of the control parameter search, and easier to select operating conditions with high evaluation costs in the later stages of the control parameter search. This allows the parameter adjustment device 1 to use a variety of operating conditions in the search.
• correction coefficient ⁇ is not limited to increasing linearly as the search progress level ⁇ increases.
• the correction coefficient ⁇ may increase in a curved line as the search progress level ⁇ increases.
• FIG. 14 is also an explanatory diagram showing an example of the operation of the parameter adjustment device 1 with respect to the objective functions of the multiple operating conditions shown in FIGS. 5A and 5B .
• (a), (b), (c), (d), and (e) of FIG. 14 are diagrams showing the same content as (a), (b), (c), (d), and (e) of FIG. 9 , respectively.
• the evaluation of the operating conditions shown in FIG. 14 incurs the evaluation cost shown in FIG. 8 .
• the selection probability of odd-numbered operating conditions shown in Figure 14(a) fluctuates around 0 in the early stages of the search (e.g., when trial is less than 20), and rises to around 1 in the middle and later stages of the search (e.g., when trial is 20 or greater), fluctuating within the range of 0 to 1.
• the cost-graded selection probability table updated based on Equation 8 above is reflected in step S255 of Figure 4. Specifically, this is because the value of the correction coefficient ⁇ increases as the search progress level ⁇ increases as shown in Figure 14(d).
• the selection probability of operating condition cond3 fluctuates at a value lower than the selection probability of operating condition cond2, which has a similar objective function, when trial is less than 20, but fluctuates at a value close to the selection probability of operating condition cond2 (near 1) when trial is 50 or greater.
• the selection probability of operating condition cond1 fluctuates at a value lower than the selection probability of operating condition cond0, which has a similar objective function, when trial is less than 20, but fluctuates at a value close to the selection probability of operating condition cond0 (within the range of 0 to 1) when trial is 50 or greater.
• the selection probability of operating conditions cond5 and cond7 remains lower than the selection probability of operating conditions cond4 and cond6, which have similar objective functions, when trial is less than 20. However, when trial is 50 or greater, it remains close to the selection probability of operating conditions cond4 and cond6 (within the range of 0 to 1).
• the evaluation value shown in FIG. 14(c) is improved from the beginning of the search, preventing stagnation in the early stages of the search, similar to the effect of the evaluation value shown in FIG. 9(c).
• the selection probability update unit 125 sets the selection probability table according to the progress of the search so that the selection probability of operating conditions (cond2 and cond3) that are not below the reference value among the multiple operating conditions (cond0 to 7) is increased.
• this parameter adjustment method can reduce the total evaluation cost in the search while avoiding the evaluation of operating conditions that have a high evaluation cost.
• FIG. 15 is a diagram showing the transition of the evaluation value when focusing on operation conditions cond2 and cond3.
• (a) of FIG. 15 is a diagram showing the transition of the evaluation value obtained in operation example 2.
• (b) of FIG. 15 is a diagram showing the transition of the evaluation value obtained in a modified example of operation example 2.
• (a) of FIG. 15 and (b) of FIG. 15 are diagrams in which the horizontal axis represents the cumulative evaluation cost and the vertical axis represents the evaluation value.
• (c) of FIG. 15 is a diagram showing the transition of the search progress degree ⁇ with respect to the cumulative evaluation cost.
• the parameter adjustment device 1 can increase the number of times it searches for control parameters that simultaneously improve the operating conditions cond2 and cond3, which are bottlenecks where maximum values are likely to appear, in the modified version of operation example 2 compared to operation example 2.
• the parameter adjustment device 1 increases the number of times it simultaneously selects the operating conditions cond2 and cond3, which are bottlenecks where maximum values are likely to appear, and can therefore increase the number of times it searches for control parameters that simultaneously improve the operating conditions cond2 and cond3.
• the parameter adjustment device 1 can search for control parameters that will make the maximum value of the obtained evaluation values fall below (satisfy) the reference value, with less evaluation cost.
• Fig. 16 is a diagram showing a modified example of the image shown in Fig. 11.
• the same components included in the image shown in Fig. 11 are denoted by the same reference numerals and description thereof will be omitted, and components not included in the image shown in Fig. 11 are denoted by new reference numerals and description will be omitted.
• this image differs from the image shown in Figure 11 in that it includes a pass criteria input area 413 and a pass rate display area 414.
• the user can provide the pass criteria for the set operating conditions to the input/output unit 11 via the pass criteria input area 413. This causes the parameter adjustment device 1 to search for control parameters that will cause the evaluation value of each set operating condition to fall below the pass criteria (6.0 in the example of FIG. 16).
• the horizontal axis represents each trial from the start of control parameter adjustment to the present, and an image is displayed showing the progress of the search ⁇ , calculated by the selection probability update unit 125 using the above formulas 6 and 7, as a line graph.
• the parameter adjustment device 1 can accept the user's specification of pass criteria via the input/output device 3.
• the parameter adjustment device 1 is a parameter adjustment device 1 that searches for optimal control parameters for operating the control system 2 under a plurality of operating conditions by operating the control system 2 while adjusting the control parameters, and includes: a condition setting unit 121 that sets at least one operating condition and control parameters among the plurality of operating conditions to the control system 2; an evaluation unit 122 that calculates, for each of the at least one operating condition, an evaluation value related to the operation of the control system 2 when the control system 2 operates under the at least one operating condition and control parameters set by the condition setting unit 121; a comprehensive evaluation unit 123 that calculates, as a comprehensive evaluation value, a weighted average value obtained using at least the maximum and minimum values of the evaluation values calculated by the evaluation unit 122; and an optimization unit 124 that calculates, using an optimization algorithm, control parameters for the next operation of the control system 2 based on the comprehensive evaluation value calculated by the comprehensive evaluation unit 123.
• the condition setting unit 121 sets at least one operating condition and the control parameters calculated by the optimization unit
• the overall evaluation unit 123 calculates the weighted average of the maximum and minimum values of multiple evaluation values as the overall evaluation value, making it possible to obtain a variety of overall evaluation values in each search operation.
• the overall evaluation unit 123 calculates the overall evaluation value as a weighted average obtained using a plurality of upper evaluation values including the maximum value among the evaluation values and a plurality of lower evaluation values including the minimum value among the evaluation values.
• the overall evaluation unit 123 calculates the weighted average of multiple evaluation values, including maximum and minimum evaluation values, as the overall evaluation value, allowing for a wider variety of overall evaluation values to be obtained in each search operation.
• the parameter adjustment device 1 further includes a selection probability update unit 125 that updates a selection probability table based on the evaluation value calculated by the evaluation unit 122.
• the selection probability table sets the probability of selecting the operating condition with the maximum evaluation value higher than the probability of selecting other operating conditions, and the probability of selecting the operating condition with the minimum evaluation value according to the progress of the search.
• the selection probability update unit 125 selects at least one operating condition from multiple operating conditions based on the selection probability table updated by the selection probability update unit 125.
• the condition setting unit 121 sets the at least one operating condition selected by the operating condition selection unit 126 and the control parameters calculated by the optimization unit 124 in the control system 2.
• the parameter adjustment device 1 performs search operations using the operating conditions selected by the operating condition selection unit 126, thereby reducing the number of evaluations required for each search operation. This allows the parameter adjustment device 1 to reduce the time required for performance evaluation using simulations or actual machines when automatically adjusting control parameters for multiple operating conditions.
• the selection probability table is set so that the probability of selecting the top operating conditions that include the maximum evaluation value is higher than the probability of selecting other operating conditions, and the probability of selecting the bottom operating conditions that include the minimum evaluation value is set according to the progress of the search.
• the selection probability table is set with the probability of selecting multiple operating conditions with maximum and minimum evaluation values, allowing the operating condition selection unit 126 to select a variety of operating conditions.
• the parameter adjustment device 1 performs each search operation using a variety of operating conditions, allowing the overall evaluation unit 123 to obtain a variety of overall evaluation values. Therefore, the parameter adjustment device 1 can avoid adjustment stagnation at the beginning of the search, thereby reducing the time required for performance evaluation using simulation or an actual machine when automatically adjusting control parameters for multiple operating conditions.
• the selection probability table is set so that when the degree of search progress is greater than the threshold, the probability of selecting the operating condition with the smallest evaluation value is lower than when the degree of search progress is less than the threshold.
• the operating condition selection unit 126 is more likely to select the operating condition with the smallest value, and the parameter adjustment device 1 is more likely to perform search operations that include that operating condition.
• the optimization unit 124 can calculate the next control parameter to be tried using the overall evaluation value of various values as a hint, allowing the parameter adjustment device 1 to avoid adjustment stagnation.
• the operating condition selection unit 126 is less likely to select the operating condition with the smallest value, and the parameter adjustment device 1 is less likely to perform search operations that include that operating condition. This allows the parameter adjustment device 1 to reduce the number of evaluations required for each search operation. Therefore, the parameter adjustment device 1 can reduce the time required for performance evaluation using simulation or an actual device when automatically adjusting control parameters for multiple operating conditions.
• the selection probability update unit 125 further updates a cost-graded selection probability table in which the probabilities set in the selection probability table are weighted by the evaluation cost
• the operating condition selection unit 126 further selects at least one operating condition based on the cost-graded selection probability table updated by the selection probability update unit 125.
• This configuration makes it easier for the operating condition selection unit 126 to select operating conditions with low evaluation costs and less likely to select operating conditions with high evaluation costs, allowing the parameter adjustment device 1 to reduce the evaluation costs associated with each search operation. Furthermore, because the parameter adjustment device 1 performs search operations using operating conditions selected by the operating condition selection unit 126, it is possible to reduce the number of evaluations required for each search operation. This allows the parameter adjustment device 1 to reduce the time required for performance evaluation using simulations or actual machines when automatically adjusting control parameters for multiple operating conditions.
• the selection probability update unit 125 corrects the evaluation cost according to the progress of the search.
• This configuration allows the parameter adjustment device 1 to use a variety of operating conditions in the search, thereby improving the accuracy of the control parameters.
• the parameter adjustment device 1 further includes an input/output unit 11 that receives information about setting conditions related to the adjustment of control parameters from the input/output device 3 and outputs information about the results of the adjustment of the control parameters to the input/output device 3, and at least one of the condition setting unit 121, evaluation unit 122, overall evaluation unit 123, and optimization unit 124 operates using the information about the setting conditions received by the input/output unit 11, and the results of the adjustment of the control parameters include at least one operating condition set in the control system 2 by the condition setting unit 121 and the control parameters calculated by the optimization unit 124.
• the parameter adjustment device 1 can output the optimal control parameters obtained through the search to the input/output device 3.
• the selection probability update unit 125 determines whether the evaluation value calculated by the evaluation unit 122 is successful or not based on whether it is below a reference value, and updates the progress of the search based on whether the evaluation value is successful or not.
• the parameter adjustment device 1 sets the selection probability table so that the probability of selecting an operating condition that does not fall below the reference value among multiple operating conditions increases depending on the progress of the search, thereby increasing the number of times multiple operating conditions that become bottlenecks and are likely to have maximum values are simultaneously selected.
• the parameter adjustment device 1 can increase the number of times it searches for control parameters that simultaneously improve operating conditions that do not fall below the reference value. This allows the parameter adjustment device 1 to search for control parameters that will bring the maximum of the obtained evaluation values below the reference value, with less evaluation cost.
• the parameter adjustment method is a parameter adjustment method executed by a computer that searches for optimal control parameters for operating control system 2 under multiple operating conditions by operating control system 2 while adjusting control parameters, and includes: a condition setting step that sets at least one of multiple operating conditions and control parameters in control system 2; an evaluation step that calculates an evaluation value related to the operation of control system 2 when control system 2 operates under at least one operating condition and control parameter set in the condition setting step; a comprehensive evaluation step that calculates a weighted average value obtained using at least the maximum and minimum values of the evaluation values calculated in the evaluation step as an overall evaluation value; and an optimization step that uses an optimization algorithm to calculate control parameters for the next operation of control system 2 based on the overall evaluation value calculated in the comprehensive evaluation step; and in the condition setting step, at least one operating condition and the control parameters calculated in the optimization step are set in control system 2.
• the weighted average of the maximum and minimum values of multiple evaluation values is calculated as the overall evaluation value, making it possible to obtain a variety of overall evaluation values in each search operation.
• the overall evaluation values obtained in each search operation can be used as a hint to calculate the next control parameters to be tried. Therefore, the parameter adjustment method can avoid adjustment stagnation in the early stages of the search, and can reduce the time required for performance evaluation using simulation or an actual device when automatically adjusting control parameters for multiple operating conditions.
• the program according to this embodiment causes a computer to execute a parameter adjustment method.
• a computer can reduce the time required for performance evaluation using simulations or actual equipment when automatically adjusting control parameters for multiple operating conditions.
• the parameter adjustment device may be used to automatically adjust control parameters in a simulation, or may be used to automatically adjust control parameters in an actual device.
• each component may be configured with dedicated hardware, or may be realized by executing a software program appropriate for that component.
• Each component may also be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.
• the functions of the parameter adjustment device according to the above embodiments may be realized by a processor such as a CPU executing a program.
• each of the above devices may be configured as an IC card or a standalone module that can be attached to or detached from each device.
• the above IC card or module is a computer system consisting of a microprocessor, ROM, RAM, etc.
• the above IC card or module may include an ultra-multifunctional LSI.
• the above IC card or module achieves its functions when the microprocessor operates in accordance with a computer program. This IC card or module may be tamper-resistant.
• the parameter adjustment device disclosed herein is useful, for example, as a device for searching for optimal control parameters.

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