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
  • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
  • G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
• • 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
  • G05B19/00—Programme-control systems
  • G05B19/02—Programme-control systems electric
  • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
  • G05B19/408—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by data handling or data format, e.g. reading, buffering or conversion of data

Definitions

• the invention relates to a device and a method for determining control device parameters.
• application or application data In the area of ECU application or data determination, the properties and the behavior of the vehicle system are provided by application or application data. These application parameters, which are also referred to as control unit parameters, thus influence the driving behavior of the vehicle, since these define the functions of the control unit and thus the functioning of the control unit.
• control unit functions make it possible to determine fixed settings by means of a parameter set, in many cases also by means of several parameter sets, via constants, characteristic curves and characteristic diagrams. It should be noted that the complexity of the functions and thus the number of maps is constantly increasing. Function specialists who know the influence of each parameter at best can interpret the functions according to the requirements of the customer.
• a specific behavior for example the driving behavior
• the controller parameters that are responsible for a desired behavior may interfere with each other.
• the functions and the parameters influencing these are hardly comprehensible. Furthermore, it should be noted that the complexity of the functions and thus also the number of functional parameters increase with increasing demands on the vehicle.
• Target variables relate to a desired behavior of the motor vehicle, for example in terms of ride comfort and dynamics.
• time constants, amplification factors and triggering thresholds serve as functional parameters for this purpose.
• the injection pressure, the rail pressure, the exhaust gas recirculation and the valve position serve as the functional parameters or are derived therefrom.
• the user it is possible for the user to specify the desired behavior via a weighting or via weightings of target variables.
• the function parameters are no longer set by the processor but the behavior to be influenced by the function.
• a driving behavior application by means of a user interface parts or a human-machine interface, to set the vehicle behavior virtually continuously.
• a slider or slider of a human-machine interface or GUI is used.
• the actual function parameters are set by an algorithm based on the slider positions.
• a rotary knob or other devices that allow for continuous adjustment could also be used.
• a possible embodiment or extension of this approach provides that this quasi-continuous adjustment is offered to the end customer as a vehicle feature or feature.
• the required data may be provided by a separately available storage medium, such as USB, DVD, SD, etc.
• the described method achieves a simpler application of the functional or control device parameters compared with known methods. In addition, a consistently high quality of the application is guaranteed. In particular, if only optimal parameters are used, there is a precise knowledge of limits and possibilities of the system. Furthermore, there is the possibility that the OEM or the end customer, at least within certain limits, can stop the behavior itself.
• the method it is possible to determine the sets of optimal parameters in a so-called statistical experimental design (DoE: Design of Experiments). These changes are made and recorded using a test / measurement automation. The test results are evaluated on the basis of specific evaluation criteria. To be observed The relationship between the application parameters and the evaluation criteria can be mapped in a model. By means of this model, a multi-criteria optimization task with regard to the evaluation criteria can be carried out. The result is a set of optimal application datasets that can be set, for example, through the use of sliders in a GUI.
• DoE Design of Experiments
• the parameters can also be determined by an optimization on a physical model or by an online optimization on the real system.
• the parameter sets can be stored in a so-called model of optimal parameters.
• a multi-objective optimization on all necessary target variables and / or criteria with the available functional parameters is carried out in advance directly on the system or on a behavioral model of the system (corresponding criteria model) over all necessary operating points with an optimizer.
• the results obtained from the optimization then contain for each operating point the optimized function parameters for all compromises of the target values and / or criteria.
• an operating point-dependent model of optimal parameters can then be created. This can be done in the form of maps and multi-dimensional data models.
• the inputs of the model are the operating points and the target values and / or criteria or their weighting and thus the weighting of the target values / criteria.
• the simplest form of representation of optimal parameters is via a list in which the optimization results are stored on the operating points and on various compromises of the evaluation criteria.
• the selection of a desired behavior can be done very quickly and easily.
• the procedure can be applied to many application tasks.
• the know-how or knowledge about the ECU function and the application knowledge can be compact in this go to be merged.
• the customer eg the OEM or the end customer, can easily adjust the behavior himself.
• FIG. 1 shows a procedure for determining optimal parameters by means of a statistical experimental design.
• FIG. 2 shows the procedure from FIG. 1, with a criteria-related application being highlighted.
• Figure 3 shows a schematic representation of an embodiment of the device described.
• FIG. 1 shows a behavioral model or parameter-criteria model 10 into which control device parameters 12 are input, which are interpreted in the model 10 by means of an interpreter 22, so that criteria 16 F s and T a result.
• a behavior model or data model 24 is created.
• an optimizer 28 can be optimized and the results can be displayed or visualized with a visualizer 30. Based on this data, the behavioral model 24 of the system can be calculated. Optimized ECU parameters 34 are then made available to an application 36, in this case a motor vehicle.
• FIG. 2 shows a representation of the determination of optimal parameters comparable to FIG. 1 by means of a DoE test plan.
• a criteria-based application 50 is shown which operates on the basis of optimal parameters and the target conflict 52 between the evaluation criteria, for example dynamic comfort.
• the optimal parameters and the conflict of goals between the criteria are results of the optimizer 28. Through a user interface, one can choose a compromise in the target conflict.
• the corresponding parameters are stored and set in the control unit.
• control unit or application parameters 12 are thus changed by means of the statistical experimental design (DoE) 18.
• DoE statistical experimental design
• changes are carried out using a test / measurement automation 20 and recorded. Resulting test results are then evaluated on the basis of specific evaluation criteria.
• a relationship between the ECU parameters 12 and the evaluation criteria are mapped in a model 24.
• a multi-criteria optimization can be carried out taking into account the evaluation criteria.
• FIG. 3 shows, in a schematic illustration, an embodiment of the device for determining functional parameters, which is denoted overall by the reference numeral 100.
• This device 100 can be implemented, for example, by a computer program that can be executed within a control unit software.
• the device 100 is connected to a control unit 102 and has a user interface 104, on which a user interface 106, which is designed as a graphical user interface in the form of a slide control, is provided.
• target values that relate to a behavior of a vehicle, or also weightings of target values can be predetermined by a user.
• the target variables or the weights of target variables to be selected are assigned to sets 108 and 110 of parameters 1 12 and 1 14 or 1 16 and 1 18, which are stored in a memory unit 1 19.

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• Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Physics & Mathematics (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Manufacturing & Machinery (AREA)
• Health & Medical Sciences (AREA)
• Artificial Intelligence (AREA)
• Human Computer Interaction (AREA)
• Evolutionary Computation (AREA)
• Medical Informatics (AREA)
• Software Systems (AREA)
• Feedback Control In General (AREA)
• Combined Controls Of Internal Combustion Engines (AREA)
• Automatic Analysis And Handling Materials Therefor (AREA)
• Stored Programmes (AREA)

Applications Claiming Priority (2)

Publications (1)

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• 2009
  • 2009-12-17 DE DE102009054900A patent/DE102009054900A1/de active Pending
• 2010
  • 2010-12-02 EP EP10787740A patent/EP2513726A1/de not_active Withdrawn
  • 2010-12-02 CN CN201080057288.6A patent/CN102652291B/zh active Active
  • 2010-12-02 WO PCT/EP2010/068757 patent/WO2011082909A1/de active Application Filing
  • 2010-12-02 IN IN3267DEN2012 patent/IN2012DN03267A/en unknown
  • 2010-12-02 US US13/515,461 patent/US20120310385A1/en not_active Abandoned

Non-Patent Citations (1)

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