CN118211432B - Simulation modeling method for overload work of semi-superconducting ultrahigh energy-saving motor - Google Patents
Simulation modeling method for overload work of semi-superconducting ultrahigh energy-saving motor Download PDFInfo
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Abstract
The invention belongs to the technical field of motor modeling, in particular relates to a simulation modeling method for overload work of a semi-superconducting ultra-high energy-saving motor, and aims to solve the problems that in the prior art, modeling cannot be performed accurately in the overload work process of the semi-superconducting ultra-high energy-saving motor, and further the response speed, stability and anti-interference capability of the semi-superconducting ultra-high energy-saving motor are poor. The system comprises: basic parameters of a semi-superconducting ultra-high energy-saving motor are obtained; modeling a dynamic equation of the semi-superconducting ultra-high energy-saving motor to obtain a semi-superconducting ultra-high energy-saving motor model; acquiring a control target of a semi-superconducting ultra-high energy-saving motor; constructing a prediction model of the semi-superconducting ultra-high energy-saving motor; and in each sampling period of the semi-superconducting ultra-high energy-saving motor, combining a control target and a prediction model to optimize, so as to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under the overload working condition. The modeling accuracy of the semi-superconducting ultra-high energy-saving motor is improved.
Description
Technical Field
The invention belongs to the technical field of motor modeling, and particularly relates to a simulation modeling method, a simulation modeling system and simulation modeling equipment for overload work of a semi-superconducting ultra-high energy-saving motor.
Background
The semi-superconducting super-high energy-saving motor is a new energy-saving motor, its structure is similar to that of traditional AC asynchronous motor, and is mainly formed from stator, rotor and bearing, etc.. The core part is a stator winding, and a special connection mode is adopted, so that current can flow in the stator winding, thereby generating a rotating magnetic field and driving the rotor to rotate. The principle of the semi-superconducting ultra-high energy-saving motor is that the characteristic of zero resistance of a superconducting material at low temperature is utilized, so that current in a stator winding can flow without loss, a stronger rotating magnetic field is generated, and the efficiency and the quality power of the motor are improved.
Compared with the traditional alternating current asynchronous motor, the semi-superconducting ultrahigh energy-saving motor has higher efficiency and lower use cost. Because of the strong rotating magnetic field, the output power and torque of the semi-superconducting ultra-high energy-saving motor are larger, so that the semi-superconducting ultra-high energy-saving motor can operate at a smaller rotating speed under the same load, and the energy consumption is reduced.
However, the modeling method in the existing stage makes the response speed, stability and anti-interference capability of the semi-superconducting ultra-high energy-saving motor poor in robustness. Based on the above, the invention provides a simulation modeling method for overload work of a semi-superconducting ultrahigh energy-saving motor.
Disclosure of Invention
In order to solve the problems in the prior art, namely, in order to solve the problems that the overload working process of the semi-superconducting ultra-high energy-saving motor cannot be accurately modeled in modeling in the prior art, and the response speed, stability and anti-interference capability of the semi-superconducting ultra-high energy-saving motor are poor. The invention provides a simulation modeling method for overload work of a semi-superconducting ultra-high energy-saving motor, which comprises the following steps:
Basic parameters of the semi-superconducting ultra-high energy-saving motor are obtained and used as input data; the input data comprises rated power, rated voltage and rated rotating speed;
combining the input data, and modeling a dynamic equation of the semi-superconducting ultra-high energy-saving motor to obtain a semi-superconducting ultra-high energy-saving motor model;
Acquiring a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
constructing a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
In each sampling period of the semi-superconducting ultra-high energy-saving motor, combining the control target and the prediction model, and optimizing through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under overload working conditions;
When the MPC algorithm is used for optimization, the method for solving and optimizing the dynamic equation is as follows:
acquiring a dynamic equation and converting the dynamic equation into a state space form to obtain a state equation; expanding the state equation into a vector form;
after the expansion, calculating a coefficient matrix of the state equation and a coefficient matrix of the control variable;
And solving the state equation through a linear programming algorithm by combining the coefficient matrix of the state equation and the coefficient matrix of the control variable.
In some preferred embodiments, constraints include current, rotational speed, rotor temperature constraints, vibration constraints, supply voltage constraints when optimizing by the MPC algorithm.
In some preferred embodiments, the method of introducing the constraints into the optimization of the MPC algorithm is:
Constructing a dynamic equation corresponding to the constraint condition;
combining a dynamic equation corresponding to the constraint condition and an upper limit and/or a lower limit corresponding to the constraint condition to construct a constraint inequality;
adding the constructed constraint inequality to the optimization:
；
Wherein, The function of the object is represented by a function of the object,Is a vector of state and control variables that are expanded,Is a coefficient matrix of the state equation after expansion,Is a coefficient matrix of the developed control variable,Is an auxiliary variable after the expansion,、The upper and lower bounds of the state and control variables, respectively, k represents the time step.
In some preferred embodiments, the weight matrix of the MPC algorithm is obtained by:
acquiring the weighted parameters of the weight matrix;
blurring the input variable corresponding to the parameter by a blurring method to obtain a blurred input variable; or the fuzzy value corresponding to the input variable corresponding to the parameter is used as the fuzzified input variable;
obtaining an output variable corresponding to the parameter and blurring the output variable to obtain a blurred output variable;
constructing a fuzzy rule based on the fuzzified input variable and the fuzzified output variable;
Based on the newly acquired blurred input variable, combining the blurred rule, and obtaining a blurred value of the output variable through a blurred reasoning method;
and defuzzifying the fuzzy value of the output variable to be used as a weight value corresponding to the parameter.
In some preferred embodiments, the blurring of the blurring value of the output variable is performed by: deblurring the fuzzy value of the output variable by a fuzzy weighted average method;
the weight function corresponding to the fuzzy weighted average method is as follows:
；
Wherein, The weight function is represented by a function of the weight,、The parameters of the weight function are respectively,Representing the input of the function.
In some preferred embodiments, when the set of the blurred input variable and the blurred output variable is represented by a membership function, the membership function is:
；
Wherein, Is the value of the membership function,Is the center point of the membership function,Is the standard deviation of the membership function.
In some preferred embodiments, the weight matrix of the MPC algorithm is obtained by:
acquiring the weighted parameters of the weight matrix;
acquiring an input variable corresponding to the parameter and a fuzzy value corresponding to the input variable;
obtaining an output variable corresponding to the parameter and blurring the output variable to obtain a blurred output variable;
constructing different fuzzy rules based on fuzzy values corresponding to the input variables and the fuzzified output variables;
Based on the newly acquired fuzzy values, combining different fuzzy rules, and obtaining fuzzy values of output variables corresponding to the different fuzzy rules by a fuzzy reasoning method to serve as first fuzzy values;
And according to the weight factors corresponding to different fuzzy rules, combining the first fuzzy value and the fuzzy degree of the different fuzzy rules to obtain a fuzzy value corresponding to the parameter, and performing defuzzification to serve as the weight value corresponding to the parameter.
In a second aspect of the present invention, a simulation modeling system for overload operation of a semi-superconducting ultra-high energy-saving motor is provided, the system comprising:
The data acquisition module is configured to acquire basic parameters of the semi-superconducting ultrahigh energy-saving motor as input data; the input data comprises rated power, rated voltage and rated rotating speed;
the modeling module is configured to combine the input data to perform dynamic equation modeling on the semi-superconducting ultrahigh energy-saving motor so as to obtain a semi-superconducting ultrahigh energy-saving motor model;
The target acquisition module is configured to acquire a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
The model construction module is configured to construct a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
the optimization module is configured to combine the control target and the prediction model in each sampling period of the semi-superconducting ultra-high energy-saving motor, and optimize the semi-superconducting ultra-high energy-saving motor through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under the overload working condition;
When the MPC algorithm is used for optimization, the method for solving and optimizing the dynamic equation is as follows:
acquiring a dynamic equation and converting the dynamic equation into a state space form to obtain a state equation; expanding the state equation into a vector form;
after the expansion, calculating a coefficient matrix of the state equation and a coefficient matrix of the control variable;
And solving the state equation through a linear programming algorithm by combining the coefficient matrix of the state equation and the coefficient matrix of the control variable.
According to a third aspect of the present invention, a simulation modeling apparatus for overload operation of a semi-superconducting ultra-high energy-saving motor is provided, which is characterized by comprising: at least one processor; and a memory communicatively coupled to at least one of the processors; the memory stores instructions executable by the processor, and the instructions are used for being executed by the processor to realize the simulation modeling method for overload operation of the semi-superconducting ultra-high energy-saving motor.
In a fourth aspect of the present invention, a computer readable storage medium is provided, where the computer readable storage medium stores computer instructions for execution by a computer to implement a simulation modeling method for overload operation of a semi-superconducting ultra-high energy-saving motor as described above.
The invention has the beneficial effects that:
The invention improves the modeling accuracy of the semi-superconducting ultra-high energy-saving motor so as to realize better response speed, stability and anti-interference capability.
1) The invention optimizes the process of solving the optimization by using the quadratic programming QP in the traditional MPC algorithm, thereby solving the problem that the QP possibly causes too high calculation complexity and cannot meet the requirement of real-time performance, and reducing the calculation time and the calculation complexity.
2) According to the invention, the control precision weight and the response speed weight are calculated by combining a fuzzy reasoning method with a state equation, constraint conditions and a weight factor, so that the optimization and adjustment of a semi-superconducting ultra-high energy-saving motor control system can be realized, better control performance and response characteristics are achieved, and the accurate modeling of a controller of the semi-superconducting ultra-high energy-saving motor in the overload working process is realized. The fuzzy reasoning method can help the control system of the semi-superconducting ultra-high energy-saving motor to make intelligent decisions in a complex environment so as to meet different control requirements and performance indexes.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings.
Fig. 1 is a schematic flow chart of a simulation modeling method for overload operation of a semi-superconducting ultra-high energy-saving motor according to a first embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The invention relates to a simulation modeling method for overload operation of a semi-superconducting ultra-high energy-saving motor, which is shown in fig. 1, and comprises the following steps:
Basic parameters of the semi-superconducting ultra-high energy-saving motor are obtained and used as input data; the input data comprises rated power, rated voltage and rated rotating speed;
combining the input data, and modeling a dynamic equation of the semi-superconducting ultra-high energy-saving motor to obtain a semi-superconducting ultra-high energy-saving motor model;
Acquiring a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
constructing a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
In each sampling period of the semi-superconducting ultra-high energy-saving motor, combining the control target and the prediction model, and optimizing through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under overload working conditions;
When the MPC algorithm is used for optimization, the method for solving and optimizing the dynamic equation is as follows:
acquiring a dynamic equation and converting the dynamic equation into a state space form to obtain a state equation; expanding the state equation into a vector form;
after the expansion, calculating a coefficient matrix of the state equation and a coefficient matrix of the control variable;
And solving the state equation through a linear programming algorithm by combining the coefficient matrix of the state equation and the coefficient matrix of the control variable.
In order to more clearly describe the simulation modeling method for overload operation of the semi-superconducting ultra-high energy-saving motor, each step in one embodiment of the method is described in detail below with reference to the accompanying drawings.
The existing motor modeling method comprises the following steps: determining basic parameters of the motor: the method comprises rated power, rated voltage, rated rotating speed and the like of a motor; and determining overload working conditions of the motor: according to actual requirements, determining the load condition of the motor when in overload work, such as work exceeding rated power; establishing a mathematical model of the motor: according to the physical characteristics of the motor, a mathematical model of the motor is established, wherein the mathematical model comprises a dynamic equation of the motor, a transfer function of the motor and the like; and (3) performing simulation experiments: and constructing a simulation model of the motor by using simulation software (such as MATLAB/Simulink), and setting overload working conditions of the motor. Through simulation experiments on the motor, performance indexes of the motor under overload working conditions, such as current, rotating speed, power and the like, can be obtained; analyzing simulation results: according to the simulation result, the performance of the motor under the overload working condition, such as the loss condition, the temperature rise condition and the like of the motor, is analyzed. The reliability and stability of the motor under overload working conditions can be evaluated, but the response speed, stability and anti-interference capability are poor. Based on the above, the invention provides a simulation modeling method for overload work of a semi-superconducting ultrahigh energy-saving motor, which comprises the following steps:
Basic parameters of the semi-superconducting ultra-high energy-saving motor are obtained and used as input data; the input data comprises rated power, rated voltage and rated rotating speed;
combining the input data, and modeling a dynamic equation of the semi-superconducting ultra-high energy-saving motor to obtain a semi-superconducting ultra-high energy-saving motor model;
The modeling of the dynamic equation of the semi-superconducting ultra-high energy-saving motor comprises two methods, wherein one is a motor dynamic equation, and the other is a transfer function method. In this embodiment, motor dynamic equation modeling is preferred, and in other embodiments other methods of modeling may be selected.
Modeling a dynamic equation: assuming the motor is a DC motor, modeling can be performed using classical DC motor dynamic equations. The dynamic equation describes the relationship between the electromagnetic torque and the mechanical torque of the motor, which can be expressed as:
Wherein, For the angle of the rotor of the motor,In order for the moment of inertia to be of interest,Is the second derivative of the rotor angle,As a result of the damping coefficient of the rotor,Is the first derivative of the angle of the rotor,Is a torque constant of the motor and is used for controlling the motor,For the motor current to be equal to,Is the load torque.
Modeling transfer functions: the transfer function modeling method may represent a relationship between an input and an output of the motor as one transfer function. Assuming that the motor is a direct current motor, a transfer function modeling method may be used. The transfer function can be expressed as:
Wherein, As a function of the transfer of the motor,In order to gain of the transfer function,In order to be a time constant of the transfer function,Is a complex number.
In order to further enable the semi-superconducting ultra-high energy-saving motor to have better response speed, stability and anti-interference capability. The invention designs a new control algorithm aiming at overload working conditions of the motor, namely, an MPC algorithm is adopted to generate an optimal control strategy by predicting the future state and output of the motor based on a mathematical model of the motor.
Firstly, the dynamic equation of the built motor is converted into a state space form, namely，. Wherein,Is the state vector, i.e. the input vector,Is a control input which is used to control the operation of the device,Is the output and A, B, C, D is the set parameter.
Acquiring a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
in this embodiment, a control target of the motor, such as speed or position tracking, is set according to the demand under the overload operation condition.
Constructing a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
In this embodiment, a prediction model is designed based on the state space of the semi-superconducting ultra-high energy-saving motor, for predicting the future state and output of the motor. A discrete time state space model may be used in combination with the actual sampling period of the motor.
And in each sampling period of the semi-superconducting ultra-high energy-saving motor, combining the control target and the prediction model, and optimizing through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under the overload working condition.
In this embodiment, the control objective and the prediction model of the motor are incorporated into an optimization problem, and an optimal controller is obtained by solving the optimization problem. The objective of the optimization problem is to minimize the prediction error and the amount of change in the control input while meeting constraints under overload operating conditions. In each sampling period, an optimal control input is calculated according to the current motor state and the prediction model and is applied to a motor control system.
The advantage of the MPC algorithm is that it can be optimized on-line during each sampling period to accommodate variations in overload operating conditions. It can provide better response speed, stability and anti-interference capability. At the same time, the MPC algorithm may also take into account constraints such as maximum current, maximum rotational speed, etc.
It should be noted that the specific MPC algorithm design needs to be properly tuned and optimized according to the characteristics of the motor and the overload operating conditions. In addition, in order to meet the creative requirements of the patent, innovative ideas such as new optimization algorithms, constraint conditions or variable weight adjustment strategies can be introduced into the MPC algorithm.
1) New optimization algorithm: conventional MPC algorithms typically use Quadratic Programming (QP) to solve the optimization problem. However, under overload operating conditions, QP may result in excessive computational complexity that cannot meet the real-time requirements. Thus, the introduction of non-linear programming (NLP) or approximation-based optimization algorithms may be considered to replace conventional QP. These algorithms can reduce computation time and complexity while guaranteeing control performance.
In the present invention, the optimization is performed by the following optimization algorithm:
In a conventional MPC algorithm, the optimization problem can be expressed generally in the form of:
Wherein, Is a performance index (usually an objective function, usually used for measuring the performance, efficiency or other key indexes of a system, and for overload work simulation modeling of a superconducting energy-saving motor, the performance index can be efficiency, power factor, temperature stability and the like of the motor),Is a state variable that is a function of the state,Is a control variable which is used to control the operation of the device,AndIs a matrix of coefficients of the state equation,AndIs the upper and lower bounds of the state variable,AndIs the upper and lower bound of the control variable.
Expanding the state variables and the control variables to obtain the following optimization problems:
Wherein, Is a vector of state and control variables that are expanded,Is a coefficient matrix of the developed state equation,Is a coefficient matrix of the expanded control variable,Is an auxiliary variable of expansion. The unfolded variable and coefficient matrix can be obtained by calculation according to the original model.
The unfolded optimization problem is converted into a linear programming problem, and can be solved by using a linear programming algorithm (such as a simplex method). The linear programming algorithm has lower calculation complexity and can obtain an optimization result in a shorter time. The optimization algorithm can reduce the calculation complexity and improve the real-time performance of the motor under the overload working condition on the premise of ensuring certain control performance. In general, the model predictive simplification algorithm can help optimize the control strategy in simulation modeling of overload work of the superconducting energy-saving motor, and improve the performance and efficiency of the system. By converting the complex nonlinear optimization problem into a simplified linear programming problem, the system optimization control can be realized more efficiently.
For example: taking a simplified motor model as an example to illustrate the expansion method and the calculation of coefficient matrix
Assuming that a control problem of a direct current motor is considered, the motor model can be expressed as the following state equation:、 wherein, the method comprises the steps of, wherein, The state vector x (k) is a state vector of the motor, and usually comprises a minimum group of variables in the system, so that the state of the system can be completely described, including states of motor rotation speed, current and the like; Is a control input vector comprising a voltage input of the motor; Is an output vector representing the rotational speed of the motor, k represents a discrete time step, represents the state and input of the system at the kth time step, C is a coefficient matrix of the output equation, and in the motor model, a and B represent the state vector and the coefficient matrix of the control input vector in the state equation, respectively. Specifically, a represents the law of evolution of the system state vector over a time step, and B represents the effect of the control input vector on the system state over a time step. A and B are typically derived based on the physical principles and mathematical modeling of the system. For example, in a model of a DC motor, A and B may be derived from the motor's circuit equations and mechanical equations, describing the relationship between motor state and control inputs.
The state and control inputs are expanded into vector form: Is a combination vector that includes the state and control inputs of the system to describe the complete state of the system at each time step.
The state equation after expansion can be expressed as:；；
Wherein, Is a coefficient matrix of the state equation after expansion,Is a coefficient matrix of the developed control variable,Is an auxiliary variable after expansion; coefficient matrix of state equation after expansionAndThe calculation may be performed according to the developed state equation. In this example of the present invention, in this case,AndIs calculated as follows:、 wherein, the method comprises the steps of, wherein, Is an identity matrix.
The motor model is obtained based on the physical principle and mathematical modeling of the motor, the state vector and the control input vector are combined vectors for describing the state and the control input of the system, and the coefficient matrix C in the output equation is used for describing the relation between the output and the state of the system. In model predictive reduction algorithms, expanding state and control inputs into a larger vector may facilitate processing of state and inputs of the system.
Calculating to obtain coefficient matrix of state equation after expansionAndThe optimization problem can be converted into a linear programming problem, and the linear programming problem is solved by using a linear programming algorithm. It should be noted that the specific deployment method and the calculation of the coefficient matrix need to be adjusted according to the specific motor model and the control requirements. This is a simplified example, and the actual application needs to be calculated and adjusted according to the specific situation.
2) New constraints: in addition to conventional constraints, such as current, rotational speed, etc., it may be considered to introduce some new constraints to further protect the safety of the motor and system. For example, consider introducing rotor temperature constraints to prevent overheating of the motor during overload operation; considering introducing vibration constraint to avoid excessive vibration of the motor under high load; the introduction of power supply voltage constraint is considered to ensure that the motor works normally under the condition that the power system is unstable:
constraints can be represented by adding corresponding inequality constraints. The method comprises the following steps:
Constructing a dynamic equation corresponding to the constraint condition; combining a dynamic equation corresponding to the constraint condition and an upper limit and/or a lower limit corresponding to the constraint condition to construct a constraint inequality; adding the constructed constraint inequality to the optimization:
Wherein, The function of the object is represented by a function of the object,Is a vector of state and control variables that are expanded,Is a coefficient matrix of the state equation after expansion,Is a coefficient matrix of the developed control variable,Is an auxiliary variable after the expansion,、The upper and lower bounds of the state and control variables, respectively, k represents the time step.
Taking the rotor temperature constraint as an example, assume that the upper limit of the rotor temperature of the motor isThe temperature constraint can be expressed as a constraint conditionWhereinIs the temperature of the motor rotor. It is assumed that the rotor temperature of the motor has an important influence on the safety of the motor in consideration of the motor model and control problems. It is desirable to limit the motor rotor temperature to no more than a preset upper limit during overload operation。
To add rotor temperature constraints to the optimization problem, the temperature constraints need to be translated into inequality constraints. The dynamic equation assuming rotor temperature can be expressed as:
Wherein, Is the temperature of the rotor, which is the temperature of the rotor,Is a state variable of the motor and,Is a control variable of the motor and is used for controlling the motor,Is a dynamic equation for rotor temperature.
To express the rotor temperature constraint as an inequality constraint, a new variable may be introducedThe rotor temperature at the next time is indicated. The rotor temperature constraint can then be expressed as an inequality constraint:。
adding rotor temperature constraints to the optimization problem, the following form of optimization problem can be obtained;
By adding the rotor temperature constraint into the optimization problem, the motor can be protected from overheating during overload operation, and the safety of the motor is improved.
In addition to rotor temperature constraints, other constraints may be added according to specific requirements, such as vibration constraints, supply voltage constraints, and the like. The specific constraints and form of optimization problem need to be defined and adjusted according to the specific situation:
Vibration constraint: given that the motor under consideration may generate excessive vibrations under high load, it is desirable to limit the amplitude of the vibrations of the motor to a preset upper limit in order to protect the motor and the system from safety . The vibration constraint may be expressed as an inequality constraint:， is the vibration amplitude of the motor. Adding vibration constraints to the optimization problem can result in the following form of optimization problem:
Supply voltage constraints: assuming that the motor under consideration also needs to work normally in the case of unstable power system, it is desirable to limit the fluctuation range of the power supply voltage not to exceed the preset upper and lower limits in order to ensure the stability of the motor And. The supply voltage constraint may be expressed as an inequality constraint:
adding supply voltage constraints to the optimization problem can result in the following form of optimization problem:
3) The weight matrix in the MPC algorithm is typically used to balance the weight relationship between different control objectives and constraints. To accommodate performance requirements under overload operating conditions, the introduction of adaptive variable weight adjustment strategies may be considered. The strategy can adjust the weight matrix in real time according to the working state of the motor and the system requirement so as to optimize the control performance. For example, the weight matrix may be adaptively adjusted according to factors such as a load, a rotational speed, and the like of the motor to improve response speed or stability.
In a weight matrixIn the case of an example of this,For measuring the importance of the control object. Under overload working conditions, the weight matrix can be adaptively adjusted according to factors such as motor load, rotating speed and the like, so that response speed or stability is improved. For example, the motor load can be dynamically adjusted according to the change of the motor loadIs made to pay more attention to control accuracy when the load is large and to response speed when the load is small.
The specific adaptive weight adjustment strategy can be designed according to the characteristics of the motor and overload working conditions. For example, adaptive adjustment of the weight matrix may be achieved using methods based on fuzzy logic control or adaptive control theory.
Adjusting weight matrix using fuzzy logic controlThe process is as follows:
Acquiring the weighted parameters of the weight matrix; blurring the input variable corresponding to the parameter by a blurring method to obtain a blurred input variable; or the fuzzy value corresponding to the input variable corresponding to the parameter is used as the fuzzified input variable; obtaining an output variable corresponding to the parameter and blurring the output variable to obtain a blurred output variable; constructing a fuzzy rule based on the fuzzified input variable and the fuzzified output variable; based on newly acquired blurred input variables, combining the fuzzy rules, obtaining blurred values of output variables through a fuzzy reasoning method, and defuzzifying the blurred values of the output variables to serve as weight values corresponding to the parameters.
Assuming that the motor we consider is under overload operating conditions, a tradeoff between control accuracy and response speed is required. We can define two blurred input variables (i.e. weighted parameters): load Rate (Load Rate) and Load Size (Load Size). The load change rate represents the change speed of the motor load, and the load size represents the motor load size.
The load change rate and the load size can be subjected to fuzzification by a certain fuzzification method, so that a fuzzified input variable set is obtained:
Wherein, 、、Etc. are load change rate variables after blurring,、、And the like are load size variables after blurring.
It is also necessary to define an output variable: weight matrixIs a value of (2). For the tradeoff of control accuracy and response speed, we can define two ambiguous output variables: control Precision Weight (Precision Weight) and response speed Weight (SPEED WEIGHT).
The control precision weight and the response speed weight can be subjected to fuzzification by a certain fuzzification method, and a fuzzified output variable set is obtained:
Wherein, 、、Etc. are control accuracy weight variables after blurring,、、And the like are response speed weight variables after blurring.
Next, we need to define a fuzzy rule to infer fuzzy values of the control accuracy weight and the response speed weight of the output from fuzzy values of the load change rate and the load size. For example:
If the Load Rate is And Load Size isThen Precision Weight isAnd SPEED WEIGHT is; If the Load Rate isAnd Load Size isThen Precision Weight isAnd SPEED WEIGHT is；……
Finally, fuzzy reasoning is needed to be carried out, and fuzzy values of the control precision weight and the response speed weight of the output are deduced according to the input load change rate, the fuzzy values of the load size and the defined fuzzy rules. Fuzzy inference methods such as fuzzy association matrices, fuzzy rule synthesis, etc. may be used. By fuzzy reasoning, the fuzzy values of the output control precision weight and the response speed weight can be obtained. Then, we can deblur according to the fuzzy values to obtain the actual weight matrixIs a value of (2). Deblurring methods such as a fuzzy weighted average method, a fuzzy maximum method, etc. may be used. By means of the self-adaptive weight adjustment strategy, the weight matrix can be dynamically adjusted according to the change of motor loadTo optimize control performance.
Further improving the fuzzy reasoning: let us assume that the load change rate we input isThe load size isTheir blur values are 0.7 and 0.4, respectively. We can use fuzzy inference methods to infer fuzzy values of the control accuracy weights and response speed weights that are output.
From the fuzzy rule 1 and the input fuzzy value, we can obtain the fuzzy value of Precision Weight asThe blur value of SPEED WEIGHT is. From the fuzzy rule 2 and the input fuzzy value, we can obtain the fuzzy value of Precision Weight asThe blur value of SPEED WEIGHT is. From the fuzzy rule 3 and the input fuzzy value, we can obtain the fuzzy value of Precision Weight asThe blur value of SPEED WEIGHT is。
Then, we can deblur according to the fuzzy values to obtain the actual weight matrixIs a value of (2). Deblurring methods such as a fuzzy weighted average method, a fuzzy maximum method, etc. may be used.
Let us assume that we use a fuzzy weighted average method for defuzzification. We can define a weight function to represent the weight corresponding to each blur value. For example, we can define a weight function of a triangle, which indicates that the weight corresponding to the blur value varies within a certain range.
The weight function is assumed to be:
Wherein, AndIs a parameter of the weight function, which is used to define the range of variation of the weights,Representing an input of a function;
From the blur values and Weight functions described above, we can calculate the deblurring value of Precision Weight and the deblurring value of SPEED WEIGHT. Assume that ，. According to the fuzzy weighted average method, the defuzzification value of Precision Weight is:
The deblurring values for SPEED WEIGHT are:
finally, we can get the actual weight matrix Is set according to the defuzzification result.
In addition, in the fuzzy inference process, a fuzzy association matrix may be used to represent the relationship between the fuzzy rule and the input variable. The fuzzy association matrix is a two-dimensional matrix in which each element represents a fuzzy relationship between an input variable and an output variable.
Let us assume that the fuzzy correlation matrix we define is as follows:
Wherein, 、、Is a fuzzy set of rates of change of the load,、、Is a fuzzy set of load sizes and,、、、、、、、、Is a fuzzy set of control precision weights.
Let us assume that the load change rate we input isThe load size isTheir blur values are 0.7 and 0.4, respectively. We can use the fuzzy correlation matrix to make fuzzy inference to infer the fuzzy value of the control accuracy weight of the output. According to the input fuzzy value, a corresponding fuzzy rule can be found through the fuzzy association matrix. In this example, the corresponding fuzzy rule is. Then, we can calculate the fuzzy value of the control accuracy weight of the output by fuzzy reasoning method according to the fuzzy rule and the fuzzy value of the input. A fuzzy rule synthesizing method such as minimum value synthesis or maximum value synthesis may be used. Let us assume that we use a minimum synthesis method. According to fuzzy rulesAnd the input fuzzy value, we can obtain the fuzzy value of the control precision weight as. Finally, we can perform defuzzification based on the fuzzy value to obtain the actual weight matrixIs a value of (2). Deblurring methods such as a fuzzy weighted average method, a fuzzy maximum method, etc. may be used.
In the further steps: membership functions may be used to represent fuzzy sets of variables, for example, gaussian membership functions may be used to represent fuzzy sets, the mathematical expression of which is:
Wherein, Is the value of the membership function,Is the value of the input variable and,Is the center point of the membership function,Is the standard deviation of the membership function.
Suppose we select three fuzzy sets of load change rates、、And fuzzy sets of three load sizes、、Their center points and standard deviations are as follows:
；；；
；；；
From the input load change rate and load magnitude, we can calculate their membership values. Assuming a load change rate of 0.7 and a load size of 0.4, their membership values can be calculated as follows:
Other membership calculation processes consistent with those described above are not repeated here.
In the further steps: let us assume that the fuzzy rule we define is as follows:
rule 1: if the Load Rate is And Load Size isThen Precision Weight isAnd SPEED WEIGHT is; Rule 2: if the Load Rate isAnd Load Size isThen Precision Weight isAnd SPEED WEIGHT is; Rule 3: if the Load Rate isAnd Load Size isThen Precision Weight isAnd SPEED WEIGHT is；
Wherein,、、Is a fuzzy set of rates of change of the load,、、Is a fuzzy set of load sizes and,、、Is a fuzzy set of control precision weights,、、Is a fuzzy set of response speed weights.
To introduce the weight of the fuzzy rules, we can assign a weight factor to each fuzzy rule. Assume that the weighting factor for rule 1 isThe weighting factor for rule 2 isThe weighting factor for rule 3 is. To introduce ambiguity of fuzzy reasoning, we can use each element of the fuzzy association matrix to represent the ambiguity of the fuzzy rule. Assuming elements of a fuzzy association matrixThe ambiguity of rule 1 is represented,The ambiguity of rule 2 is represented,Representing the ambiguity of rule 3. Let us assume that the load change rate we input isThe load size isTheir blur values are 0.7 and 0.4, respectively. We can use fuzzy inference methods to infer fuzzy values of the control accuracy weights and response speed weights that are output.
From the fuzzy rule 1 and the input fuzzy value, we can obtain the fuzzy value of Precision Weight asThe blur value of SPEED WEIGHT is. From the fuzzy rule 2 and the input fuzzy value, we can obtain the fuzzy value of Precision Weight asThe blur value of SPEED WEIGHT is. From the fuzzy rule 3 and the input fuzzy value, we can obtain the fuzzy value of Precision Weight asThe blur value of SPEED WEIGHT is。
Then, according to the weight factor and the ambiguity, the fuzzy values of the output control precision weight and the response speed weight can be calculated through a weighted fuzzy reasoning method.
Let the weight factor be，，Ambiguity is，，. According to the weighted fuzzy reasoning method, the fuzzy value of the control precision weight can be obtained as follows:
the fuzzy value of the response speed weight is:
Finally, we can deblur based on these fuzzy values to obtain the actual weight matrix Is a value of (2). Deblurring methods such as a fuzzy weighted average method, a fuzzy maximum method, etc. may be used.
After modeling, simulation test can be performed or the semi-superconducting ultra-high energy-saving motor can be controlled, and the simulation test and control method is the prior art and is not stated one by one.
In summary, in a semi-superconducting ultra-high energy-saving motor control system, a fuzzy reasoning method is combined with a state equation, constraint conditions and weight factors to calculate control precision weights and response speed weights. By combining these parts, optimization and tuning of the motor control system can be achieved to achieve better control performance and response characteristics. The fuzzy reasoning method can help the motor control system to make intelligent decisions in a complex environment so as to meet different control requirements and performance indexes.
The invention provides a simulation modeling system for overload work of a semi-superconducting ultra-high energy-saving motor, which comprises the following components:
The data acquisition module is configured to acquire basic parameters of the semi-superconducting ultrahigh energy-saving motor as input data; the input data comprises rated power, rated voltage and rated rotating speed;
the modeling module is configured to combine the input data to perform dynamic equation modeling on the semi-superconducting ultrahigh energy-saving motor so as to obtain a semi-superconducting ultrahigh energy-saving motor model;
The target acquisition module is configured to acquire a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
The model construction module is configured to construct a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
the optimization module is configured to combine the control target and the prediction model in each sampling period of the semi-superconducting ultra-high energy-saving motor, and optimize the semi-superconducting ultra-high energy-saving motor through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under the overload working condition;
When the MPC algorithm is used for optimization, the method for solving and optimizing the dynamic equation is as follows:
acquiring a dynamic equation and converting the dynamic equation into a state space form to obtain a state equation; expanding the state equation into a vector form;
after the expansion, calculating a coefficient matrix of the state equation and a coefficient matrix of the control variable;
And solving the state equation through a linear programming algorithm by combining the coefficient matrix of the state equation and the coefficient matrix of the control variable.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the method of the first embodiment for the specific working process and the related description of the system of the second embodiment.
It should be noted that, in the simulation modeling system for overload operation of the semi-superconducting ultra-high energy-saving motor provided in the above embodiment, only the division of the above functional modules is used for illustration, in practical application, the above functional allocation may be completed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are decomposed or combined again, for example, the modules in the embodiment may be combined into one module, or may be further split into a plurality of sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present invention are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present invention.
The invention relates to simulation modeling equipment for overload work of a semi-superconducting ultra-high energy-saving motor, which comprises the following components: at least one processor; and a memory communicatively coupled to at least one of the processors; the memory stores instructions executable by the processor, and the instructions are used for being executed by the processor to realize the simulation modeling method for overload operation of the semi-superconducting ultra-high energy-saving motor.
A computer-readable storage medium of a fourth embodiment of the present invention stores computer instructions for execution by a computer to implement the above-described simulation modeling method of overload operation of a semi-superconducting ultra-high energy-saving motor.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the above-described simulation modeling device for overload operation of the semi-superconducting ultra-high energy-saving motor, and the corresponding process in the foregoing system example may be referred to for no further description.
Those of skill in the art will appreciate that the various illustrative modules, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the program(s) corresponding to the software modules, method steps, may be embodied in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not intended to be limiting.
The terms "first," "second," and the like, are used for distinguishing between similar objects and not for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus/apparatus.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
Claims (7)
1. A simulation modeling method for overload work of a semi-superconducting ultra-high energy-saving motor is characterized by comprising the following steps:
Basic parameters of the semi-superconducting ultra-high energy-saving motor are obtained and used as input data; the input data comprises rated power, rated voltage and rated rotating speed;
combining the input data, and modeling a dynamic equation of the semi-superconducting ultra-high energy-saving motor to obtain a semi-superconducting ultra-high energy-saving motor model;
Acquiring a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
constructing a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
In each sampling period of the semi-superconducting ultra-high energy-saving motor, combining the control target and the prediction model, and optimizing through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under overload working conditions;
When the MPC algorithm is used for optimization, the method for solving and optimizing the dynamic equation is as follows:
acquiring a dynamic equation and converting the dynamic equation into a state space form to obtain a state equation; expanding the state equation into a vector form;
after the expansion, calculating a coefficient matrix of the state equation and a coefficient matrix of the control variable;
solving the state equation through a linear programming algorithm by combining the coefficient matrix of the state equation and the coefficient matrix of the control variable;
When the MPC algorithm is used for optimization, constraint conditions comprise current, rotation speed, rotor temperature constraint, vibration constraint and power supply voltage constraint;
The method for introducing the constraint condition into the optimization of the MPC algorithm comprises the following steps:
Constructing a dynamic equation corresponding to the constraint condition;
combining a dynamic equation corresponding to the constraint condition and an upper limit and/or a lower limit corresponding to the constraint condition to construct a constraint inequality;
adding the constructed constraint inequality to the optimization:
；
Wherein, The function of the object is represented by a function of the object,Is a vector of state and control variables that are expanded,Is a coefficient matrix of the state equation after expansion,Is a coefficient matrix of the developed control variable,Is an auxiliary variable after the expansion,、Upper and lower bounds for the state and control variables, respectively, k representing the time step;
The weight matrix of the MPC algorithm is obtained by the following steps:
acquiring the weighted parameters of the weight matrix;
blurring the input variable corresponding to the parameter by a blurring method to obtain a blurred input variable; or the fuzzy value corresponding to the input variable corresponding to the parameter is used as the fuzzified input variable;
obtaining an output variable corresponding to the parameter and blurring the output variable to obtain a blurred output variable;
constructing a fuzzy rule based on the fuzzified input variable and the fuzzified output variable;
Based on the newly acquired blurred input variable, combining the blurred rule, and obtaining a blurred value of the output variable through a blurred reasoning method;
and defuzzifying the fuzzy value of the output variable to be used as a weight value corresponding to the parameter.
2. The simulation modeling method for overload operation of a semi-superconducting ultra-high energy-saving motor according to claim 1, wherein the defuzzification of the fuzzy value of the output variable is performed by the following steps: deblurring the fuzzy value of the output variable by a fuzzy weighted average method;
the weight function corresponding to the fuzzy weighted average method is as follows:
；
Wherein, The weight function is represented by a function of the weight,、The parameters of the weight function are respectively,Representing the input of the function.
3. The simulation modeling method of overload operation of a semi-superconducting ultra-high energy-saving motor according to claim 2, wherein when the set of the blurred input variable and the blurred output variable is represented by a membership function, the membership function is:
；
Wherein, Is the value of the membership function,Is the center point of the membership function,Is the standard deviation of the membership function.
4. The simulation modeling method for overload operation of the semi-superconducting ultra-high energy-saving motor according to claim 1, wherein the weight matrix of the MPC algorithm is obtained by the following steps:
acquiring the weighted parameters of the weight matrix;
acquiring an input variable corresponding to the parameter and a fuzzy value corresponding to the input variable;
obtaining an output variable corresponding to the parameter and blurring the output variable to obtain a blurred output variable;
constructing different fuzzy rules based on fuzzy values corresponding to the input variables and the fuzzified output variables;
Based on the newly acquired fuzzy values, combining different fuzzy rules, and obtaining fuzzy values of output variables corresponding to the different fuzzy rules by a fuzzy reasoning method to serve as first fuzzy values;
And according to the weight factors corresponding to different fuzzy rules, combining the first fuzzy value and the fuzzy degree of the different fuzzy rules to obtain a fuzzy value corresponding to the parameter, and performing defuzzification to serve as the weight value corresponding to the parameter.
5. A simulation modeling system for overload operation of a semi-superconducting ultra-high energy-saving motor is characterized by comprising:
The data acquisition module is configured to acquire basic parameters of the semi-superconducting ultrahigh energy-saving motor as input data; the input data comprises rated power, rated voltage and rated rotating speed;
the modeling module is configured to combine the input data to perform dynamic equation modeling on the semi-superconducting ultrahigh energy-saving motor so as to obtain a semi-superconducting ultrahigh energy-saving motor model;
The target acquisition module is configured to acquire a control target of the semi-superconducting ultra-high energy-saving motor according to overload working conditions of the semi-superconducting ultra-high energy-saving motor; the control target comprises a speed and a position;
The model construction module is configured to construct a prediction model of the semi-superconducting ultra-high energy-saving motor based on the semi-superconducting ultra-high energy-saving motor model; the prediction model comprises state prediction and output prediction;
the optimization module is configured to combine the control target and the prediction model in each sampling period of the semi-superconducting ultra-high energy-saving motor, and optimize the semi-superconducting ultra-high energy-saving motor through an MPC algorithm to obtain an optimal controller of the semi-superconducting ultra-high energy-saving motor under the overload working condition;
When the MPC algorithm is used for optimization, the method for solving and optimizing the dynamic equation is as follows:
acquiring a dynamic equation and converting the dynamic equation into a state space form to obtain a state equation; expanding the state equation into a vector form;
after the expansion, calculating a coefficient matrix of the state equation and a coefficient matrix of the control variable;
solving the state equation through a linear programming algorithm by combining the coefficient matrix of the state equation and the coefficient matrix of the control variable;
When the MPC algorithm is used for optimization, constraint conditions comprise current, rotation speed, rotor temperature constraint, vibration constraint and power supply voltage constraint;
The method for introducing the constraint condition into the optimization of the MPC algorithm comprises the following steps:
Constructing a dynamic equation corresponding to the constraint condition;
combining a dynamic equation corresponding to the constraint condition and an upper limit and/or a lower limit corresponding to the constraint condition to construct a constraint inequality;
adding the constructed constraint inequality to the optimization:
；
Wherein, The function of the object is represented by a function of the object,Is a vector of state and control variables that are expanded,Is a coefficient matrix of the state equation after expansion,Is a coefficient matrix of the developed control variable,Is an auxiliary variable after the expansion,、Upper and lower bounds for the state and control variables, respectively, k representing the time step;
The weight matrix of the MPC algorithm is obtained by the following steps:
acquiring the weighted parameters of the weight matrix;
blurring the input variable corresponding to the parameter by a blurring method to obtain a blurred input variable; or the fuzzy value corresponding to the input variable corresponding to the parameter is used as the fuzzified input variable;
obtaining an output variable corresponding to the parameter and blurring the output variable to obtain a blurred output variable;
constructing a fuzzy rule based on the fuzzified input variable and the fuzzified output variable;
Based on the newly acquired blurred input variable, combining the blurred rule, and obtaining a blurred value of the output variable through a blurred reasoning method;
and defuzzifying the fuzzy value of the output variable to be used as a weight value corresponding to the parameter.
6. A simulation modeling device for overload operation of a semi-superconducting ultra-high energy-saving motor, comprising: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement a simulation modeling method of overload operation of a semi-superconducting ultra-high energy-saving motor according to any one of claims 1 to 4.
7. A computer readable storage medium having stored thereon computer instructions for execution by a computer to implement a simulation modeling method of overload operation of a semi-superconducting ultra-high energy-saving motor according to any one of claims 1 to 4.
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