Nonlinear compensation and control method, system and electronic device for motion platform
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2026-01-26
- Publication Date
- 2026-06-02
Smart Images
Figure CN122131586A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of ultra-precision motion control technology, and in particular to a nonlinear compensation and control method, system and electronic device for motion platforms. Background Technology
[0002] Ultra-precision motion platforms are core components of high-end equipment such as semiconductor manufacturing, and their control accuracy is severely constrained by the nonlinear behavior of the system. Traditional control methods based on fixed structure models such as classical PI converters are unable to accurately describe and compensate for the dynamic nonlinear behavior of ultra-precision motion platforms that varies with control input, resulting in a significant decrease in positioning accuracy of the platform under conditions such as high-speed or high-frequency operation. Summary of the Invention
[0003] In view of the problems existing in the prior art, the present invention provides a nonlinear compensation and control method, system and electronic device for motion platforms.
[0004] This invention provides a nonlinear compensation and control method for a motion platform, comprising: Obtain the linear variable parameter system model of the motion platform; wherein, the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; The system matrix of the linear variable parameter system model at the current moment is calculated in real time based on the measured value of the scheduling variable of the motion platform, so as to obtain the linear system model at the current moment. The predicted output sequence of the motion platform is determined by the linear system model at the current moment, and the control input of the motion platform is calculated based on the predicted output sequence.
[0005] According to the present invention, a nonlinear compensation and control method for a motion platform is provided, wherein obtaining the linear variable parameter system model of the motion platform includes: Collect at least two sets of historical data from the motion platform; the historical data includes historical inputs, historical outputs, and historical values of scheduling variables; The system matrix of the linear variable parameter system model is obtained by identifying the historical input, the historical output, and the historical values of the scheduling variables.
[0006] According to the present invention, a nonlinear compensation and control method for a motion platform is provided, wherein determining the predicted output sequence of the motion platform using a linear system model at the current moment includes: The historical output is input into the linear system model at the current moment to obtain the predicted output sequence of the motion platform output by the linear system model at the current moment.
[0007] According to the nonlinear compensation and control method for a motion platform provided by the present invention, before acquiring at least two sets of historical data of the motion platform, the method further includes: The corresponding scheduling variables are determined based on the nonlinear error of the motion platform; wherein, the scheduling variables are physical quantities that can be measured in real time.
[0008] According to the present invention, a nonlinear compensation and control method for a motion platform is provided, wherein calculating the control input of the motion platform based on the predicted output sequence includes: The control input of the motion platform is determined based on the predicted output sequence and the control target of the motion platform.
[0009] According to the present invention, a nonlinear compensation and control method for a motion platform is provided, wherein determining the control input of the motion platform based on the predicted output sequence and the control target of the motion platform includes: The control input sequence of the motion platform is determined based on the predicted output sequence and the control target of the motion platform; The first input in the control input sequence is determined as the control input of the motion platform.
[0010] The present invention also provides a nonlinear compensation and control system for a motion platform, comprising: The system model acquisition module is used to acquire the linear variable parameter system model of the motion platform; wherein, the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; The system matrix determination module is used to calculate the system matrix of the linear variable parameter system model at the current moment in real time based on the measured value of the scheduling variable of the motion platform, so as to obtain the linear system model at the current moment; The control input acquisition module is used to determine the predicted output sequence of the motion platform through the linear system model at the current moment, so as to calculate the control input of the motion platform based on the predicted output sequence.
[0011] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the nonlinear compensation and control method for a motion platform as described above.
[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the nonlinear compensation and control method for a motion platform as described above.
[0013] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the nonlinear compensation and control method for a motion platform as described above.
[0014] The present invention provides a nonlinear compensation and control method, system, and electronic device for a motion platform. By acquiring a linear variable parameter system model of the motion platform, the system matrix of the linear variable parameter system model at the current moment is calculated in real time based on the measured values of the scheduling variables of the motion platform, thus obtaining the linear system model at the current moment. The predicted output of the motion platform is determined by the linear system model at the current moment, and the control input of the motion platform is calculated based on the predicted output. By describing the dynamic nonlinear behavior between the control input and the system output through a series of linear system models that change continuously in time, the nonlinear characteristics of the motion platform can be dynamically tracked as the working conditions change over time, thereby improving the positioning accuracy of the motion platform under conditions such as high-speed or high-frequency operation. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating the nonlinear compensation and control method for a motion platform provided by the present invention.
[0017] Figure 2 This is a schematic diagram of the framework of the nonlinear compensation and control method for motion platforms provided by the present invention.
[0018] Figure 3 This is one of the schematic diagrams illustrating an example of the nonlinear compensation and control method for a motion platform provided by the present invention.
[0019] Figure 4 This is the second example of the nonlinear compensation and control method for motion platforms provided by the present invention.
[0020] Figure 5 This is the third example of the nonlinear compensation and control method for motion platforms provided by the present invention.
[0021] Figure 6 This is the fourth example of the nonlinear compensation and control method for motion platforms provided by the present invention.
[0022] Figure 7This is the fifth example of the nonlinear compensation and control method for motion platforms provided by the present invention.
[0023] Figure 8 This is the sixth example of the nonlinear compensation and control method for motion platforms provided by the present invention.
[0024] Figure 9 This is a schematic diagram of the nonlinear compensation and control method device for motion platforms provided by the present invention.
[0025] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0027] Ultra-precision motion platforms are a key technology for ultra-high precision applications such as atomic force microscopy (AFM) and scanning electron microscopy (SEM). Their performance is often limited by the complex nonlinear dynamic characteristics of the actuator or system itself. Taking an ultra-precision motion platform using a piezoelectric actuator as an example, this actuator operates based on the inverse piezoelectric effect, meaning that the piezoelectric ceramic deforms when a voltage is applied. Piezoelectric actuators can also be called piezoelectric actuators, piezoelectric movers, or PZT actuators, etc.
[0028] Piezoelectric ceramics, as ferromagnetic materials, exhibit polarization and flipping of their internal domains under the influence of an applied electric field, resulting in mechanical deformation and force. The higher the electric field strength, the more domains participate in polarization and flipping, and the more pronounced the inverse piezoelectric effect becomes. When the electric field strength reaches a point where all domains flip, the inverse piezoelectric effect tends to saturate. Since it takes time for the domain arrangement to reach a stable state, it cannot change instantaneously with the applied electric field. Furthermore, energy loss due to friction during domain flipping means that some domains cannot return to their initial state as the electric field gradually decreases, resulting in hysteresis.
[0029] This hysteresis phenomenon not only causes open-loop positioning errors of up to 10-15% in the stroke range of ultra-precision motion platforms, but also causes the shape and width of the hysteresis loop to change significantly with the frequency of the driving signal, which is the rate, exhibiting strong rate-dependent characteristics, i.e., rate-dependent hysteresis nonlinearity.
[0030] Traditional nonlinear compensation and control methods are mostly based on fixed structure models such as the classical PI model, which are difficult to accurately describe dynamic nonlinear behaviors such as rate-dependent hysteresis nonlinearity, resulting in a significant decrease in the positioning accuracy of ultra-precision motion platforms when operating over a wide bandwidth or at high speeds.
[0031] The following is combined with Figures 1 to 10 The present invention describes a nonlinear compensation and control method, system, and electronic device for a motion platform.
[0032] Figure 1 This is a flowchart illustrating the nonlinear compensation and control method for a motion platform provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps.
[0033] Step 101: Obtain the linear variable parameter system model of the motion platform.
[0034] The system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform.
[0035] A motion platform refers to a motion platform that exhibits dynamic nonlinear errors. The motion platform in this embodiment is particularly suitable for ultra-precision motion platforms. For example, the motion platform may be a piezoelectric (Lead Zirconate Titanate, PZT) driven nanostage, a magnetic levitation (Maglev) platform, a magnetostrictive actuator, a shape memory alloy (SMA) actuator, or a platform for compensating for tribolinearity, etc.
[0036] A linear variable parameter system model, also known as a linear time-varying parameter system model or linear time-varying system model, refers to a linear system model whose dynamic characteristics are reflected through time-varying parameters. The system matrix defines the input-output relationship from control input to system output under arbitrary measured values of the scheduling variable. The system matrix is a variable matrix, which is transformed into a constant matrix at specific sampling times based on the measured values of the scheduling variable.
[0037] The model framework for a linear variable parameter system model can be an auto-regressive (ARX) structure, a state-space model, an output-error model, or a Box-Jenkins model, etc., and this embodiment does not limit it.
[0038] Taking an autoregressive model structure as an example, the discrete-time form of a linear variable parameter system model can be expressed as: .
[0039] in It is system output. It controls the input. and The system matrix of a linear variable parameter system model. This represents the system's unit time delay.
[0040] For example, when the model structure is an autoregressive structure, the system matrix of a linear variable parameter system model can be expressed by the following formula: When the model structure is a state-space model, the discrete-time form of the linear variable parameter system model can be expressed as: Among these, state observers, such as Kalman filters or Luenberger observers, can be used based on measurable parameters. and To estimate the internal state in real time The subsequent actions can be based on this internal state. Predict and optimize the predicted output sequence of the motion platform.
[0041] Scheduling variables are physical quantities that can be measured in real time. Specifically, scheduling variables are one or more physical quantities that can be measured in real time and are related to the nonlinear behavior of the system at future moments. In some embodiments, the corresponding scheduling variables can be determined by the nonlinear error of the motion platform that needs to be adjusted.
[0042] For example, the scheduling variable can be the system temperature to compensate for the dynamic nonlinear changes in parameters caused by thermal drift, or it can be the output speed to compensate for the speed-dependent nonlinearity in the nonlinear friction model, etc. This embodiment does not limit this.
[0043] Step 102: Calculate the system matrix of the linear variable parameter system model at the current moment in real time based on the measured value of the scheduling variable of the motion platform, and obtain the linear system model at the current moment.
[0044] The linear system model at the current moment can also be called the linear time-invariant parameter system model at the current moment. It refers to the linear system model obtained after determining the system matrix of the linear time-invariant parameter system model based on the scheduling variables at the current moment.
[0045] It should be noted that, in order to determine the control input of the motion platform at the current moment, it is necessary to determine whether the system output corresponding to the control input of the motion platform at the current moment can meet the requirements. The system matrix of the linear time-varying parameter system model can be fixed based on the measured value of the scheduling variable at the current moment to obtain the linear system model at the current moment. Then, the corresponding system output can be determined based on the control input of the motion platform at the current moment using this linear system model, and thus, it can be determined whether it meets the requirements.
[0046] Based on the same principle, when it is necessary to determine the control input of the motion platform at the next moment, the system matrix of the linear time-varying parameter system model can be fixed according to the measured value of the scheduling variable at the next moment to obtain the linear system model at the next moment, so as to determine whether the system output corresponding to the control input at the next moment can meet the requirements.
[0047] Understandably, when the control input of the motion platform changes, the scheduling variables of the motion platform will also change accordingly. In this step, within each control cycle, the system matrix of the linear variable parameter system model at the current moment is calculated in real time using the measured values of the scheduling variables. This allows the same linear model structure to cover the nonlinear changes between control input and system output under different scheduling variables, thus obtaining a series of linear system models that change continuously over time.
[0048] Compared to nonlinear system models with fixed parameters, describing the dynamic nonlinear behavior between control input and system output using a series of linear system models that change continuously over time can dynamically track the time-varying process of the nonlinear characteristics of the motion platform as the operating conditions change. This allows for the local approximation of the actual motion platform dynamics at each moment using an optimal linear model, thereby improving the accuracy of the description of the dynamic nonlinear behavior between control input and system output and enabling nonlinear compensation of the motion platform.
[0049] Step 103: Determine the predicted output sequence of the motion platform using the linear system model at the current moment, and calculate the control input of the motion platform based on the predicted output sequence.
[0050] Among them, the predicted output of the motion platform needs to be trained to be the ideal output sequence of the motion platform at the current moment, which is determined based on the final output target.
[0051] There are many ways to determine the predicted output of the motion platform using the linear system model at the current moment, and the method can be determined according to the actual working conditions. This embodiment does not limit this method.
[0052] For example, the control input of the motion platform can be calculated by back-calculating the predicted output of the motion platform based on the linear system model at the current moment after nonlinear compensation, so as to perform actual control of the motion platform based on the control input.
[0053] The nonlinear compensation and control method for a motion platform provided in this invention obtains a linear variable parameter system model of the motion platform, calculates the system matrix of the linear variable parameter system model at the current moment in real time based on the measured values of the scheduling variables of the motion platform, obtains the linear system model at the current moment, determines the predicted output of the motion platform through the linear system model at the current moment, and calculates the control input of the motion platform based on the predicted output. By describing the dynamic nonlinear behavior between the control input and the system output through a series of linear system models that change continuously in time, the method can dynamically track the time-varying process of the nonlinear characteristics of the motion platform with the working conditions, and improve the positioning accuracy of the motion platform under high-speed or high-frequency operation.
[0054] Based on the above embodiments, obtaining the linear variable parameter system model of the motion platform includes: Collect at least two sets of historical data from the motion platform; the historical data includes historical inputs, historical outputs, and historical values of scheduling variables; The system matrix of the linear variable parameter system model is obtained by identifying the historical input, the historical output, and the historical values of the scheduling variables.
[0055] Historical data, also known as historical real data, is obtained by collecting the actual control inputs, actual system outputs, and the actual values of the corresponding scheduling variables of the motion platform.
[0056] It should be noted that the control input can be designed based on the dynamic characteristics of the expected working state of the motion platform and the range of change of the determined scheduling variables. The control input is then sent to the motion platform for experimentation, and the system output of the motion platform is collected. Parameter identification is performed based on the corresponding control input and system output, and the model parameters are determined based on the function of the scheduling variables.
[0057] For example, the control input can be a random composite sinusoidal signal. A composite sinusoidal signal is composed of multiple sinusoidal waves of different frequencies and amplitudes superimposed. This allows for precise and balanced excitation of the dynamic response under different conditions, reducing the risk of local overexcitation or underexcitation in the experimental data.
[0058] Understandably, by identifying historical inputs, historical outputs, and historical values of scheduling variables, the continuous functional relationship of scheduling variables can be learned from discrete data covering the entire system. The system matrix of the linear variable parameter system model obtained in this way can accurately describe the dynamic characteristics of the motion platform under all working states.
[0059] Traditional control methods based on classical PI and other fixed-structure models are generally open-loop feedforward compensation methods based on fixed-structure models, including offline modeling, constructing an inverse model, calculating the driving signal, and applying control. Specifically: First, historical data is obtained through experiments. Through this historical data, a complex nonlinear mathematical model with fixed model parameters is identified. This nonlinear mathematical model is located in the controller and is used to describe the nonlinear behavior of the actuator. Taking the hysteresis nonlinear behavior of PZT as an example, the nonlinear mathematical model can be the Preisach model or the Prandtl-Ishlinskii (PI) model, etc. Then, in the controller, the corresponding inverse model is obtained by mathematical derivation based on the nonlinear mathematical model. The desired reference trajectory is input into the inverse model to obtain the pre-distorted drive signal calculated and output by the inverse model. For example, a straight line can be input into the inverse model to obtain a curved voltage curve calculated and output by the inverse model. The controller can determine the feedforward command based on the predistorted drive signal and send the feedforward command to the power amplifier. The actuator and the power amplifier, and the power amplifier and the controller can be connected by electrical connection, communication connection or other means to realize signal transmission. Taking the hysteresis nonlinear behavior of PZT as an example, the power amplifier can drive the PZT platform based on the feedforward command.
[0060] It should be noted that during real-time control, due to model mismatch, external disturbances, thermal drift, or environmental changes, the model's predictions will gradually deviate from the actual situation. This control method, based on fixed-structure models such as classical PI converters, relies entirely on the accuracy of the offline model, ignoring sensor measurements during real-time control. This leads to the gradual inaccuracy of classical PI converters and other fixed-structure models, resulting in a significant decline in the long-term accuracy of the system.
[0061] Based on any of the above embodiments, determining the predicted output of the motion platform using the linear system model at the current moment and the control objective of the motion platform includes: The historical output is input into the linear system model at the current moment to obtain the predicted output sequence of the motion platform output by the linear system model at the current moment.
[0062] It should be noted that, based on the current control input, the previous control input of the motion platform, as well as the corresponding system output and scheduling variable values, can be measured and obtained to obtain historical inputs, historical outputs, and historical values of scheduling variables; and this process can be repeated to obtain historical outputs prior to the current control input. The number of historical outputs preceding the current control input can be set according to the actual operating conditions; this embodiment does not impose any limitations on this.
[0063] In some embodiments, the prediction time domain can be determined according to a pre-set time domain. The parameters of the linear variable-parameter system model at the current time step are calculated and determined using the values of the scheduling variables corresponding to the current control input, thus obtaining the linear system model frozen at the current operating point. Based on this, and combining the historical outputs prior to the current control input, the prediction time domain is obtained. The output of the motion platform within the system can be arranged in sequence to obtain the predicted output sequence.
[0064] It is understood that in this embodiment, by inputting historical output data into the updated linear system model at the current moment to predict the future output sequence of the motion platform, the real-time state feedback of the actual system can be incorporated into the prediction process. In this way, in each control cycle, the prediction is first initialized based on the latest actual measurement data, which is equivalent to introducing closed-loop correction into the prediction. This can effectively compensate for the prediction error caused by model inaccuracy and external disturbances, avoid the error accumulation problem caused by ignoring real-time measurement data in traditional feedforward models, and improve prediction accuracy and control robustness.
[0065] Based on any of the above embodiments, calculating the control input of the motion platform according to the predicted output sequence includes: The control input of the motion platform is determined based on the predicted output sequence and the control target of the motion platform.
[0066] The control objective of the motion platform, also known as the optimization direction of the motion platform, refers to the goal that the system output of the motion platform is expected to ultimately reach through a series of control inputs.
[0067] The control objective of a motion platform can be to minimize tracking error or to minimize control cost, etc. For example, the control objective of a motion platform can be a cost function in the prediction time domain defined based on tracking error and control cost.
[0068] It should be noted that an optimization problem can be constructed based on the current linear system model and the control objective of the motion platform, and the control input of the motion platform can be determined by solving this optimization problem.
[0069] Understandably, by combining the control objectives of the motion platform with the control input of the motion platform, it is possible to use a linear system model to make multi-step predictions of future dynamics, determine the impact of the current control input on the future state, and thus take corrective actions before the error actually occurs, achieving a smoother dynamic response with less overshoot.
[0070] Based on any of the above embodiments, determining the control input of the motion platform according to the predicted output sequence and the control target of the motion platform includes: The control input sequence of the motion platform is determined based on the predicted output sequence and the control target of the motion platform; The first input in the control input sequence is determined as the control input of the motion platform.
[0071] For example, it can be based on the prediction time domain The MPC optimization problem is constructed by considering the internal prediction output sequence and the control objective of the motion platform. Solving this MPC optimization problem yields the prediction time domain. Prediction time domain of the internal prediction output sequence The control sequence is then established. The first control input of this sequence is applied to the controlled motion platform, initiating the next control cycle, and the process of determining the control input is repeated.
[0072] Understandably, in this embodiment, the control input at each moment is transformed into a finite-time domain optimization problem based on the current information. By solving this finite-time domain optimization problem, the optimal control input sequence within the finite-time domain is obtained. The first input in this control input sequence is determined as the control input of the motion platform, and the subsequent control inputs of the motion platform in the control input sequence are discarded. Then, the aforementioned steps are repeated in a new control cycle for rolling optimization. Each step is based on the latest real state. In conjunction with the current linear system model, which is initialized based on the latest actual measurement data and then predicted, a closed-loop correction is performed for dual feedback, further reducing the risk of error accumulation in predictive control.
[0073] Furthermore, this embodiment accurately captures the dynamics of the motion platform through linear system models at different times and provides more accurate control input through a rolling optimization mechanism. The combination of the two can transform the nonlinear system control problem into a time-varying linear system problem, which is conducive to the application of advanced linear control theories such as MPC and reduces the design complexity of subsequent high-performance controllers.
[0074] Figure 2 This is a schematic diagram of the architecture of the nonlinear compensation and control method for a motion platform provided in this embodiment of the present invention, as shown below. Figure 2 As shown, in order to illustrate the function of the nonlinear compensation and control method for motion platforms provided in this embodiment, a specific example is provided below.
[0075] Taking a piezoelectric nanopositioning platform as the motion platform, an ARX structure as the LPV model structure, and an MPC controller as an example, the scheduling variables... The system displacement and system velocity selected from the previous time step [ .
[0076] Model coefficients of the LPV model and Identified as [ The linear functions are shown in the following formulas: After obtaining the prediction model (LPV), trajectory references at time k can be collected. and the value of the scheduling variable Input the prediction model and obtain the prediction output sequence of the prediction model. The control input of the motion platform is obtained through rolling optimization calculation. The control input Input the controlled object (including drivers and actuators), and obtain the system output of the controlled object. The position measurement values of the controlled object are collected to update the prediction model, and the above steps are repeated until the final output is completed.
[0077] In this embodiment, the results of the hysteresis model established based on the LPV model in test case (sample 1) are compared with the actual measurement results and the results of the classical PI model as follows: Figure 3 As shown, in Test Example 2, the results of the hysteresis model established based on the LPV model are compared with the actual measurement results and the results of the classical PI model. Figure 4 As shown. The error of each model prediction in test example 1 is compared to... Figure 5 As shown, the error predictions of each model in test example 2 are compared to... Figure 6 As shown. The trajectory tracking results based on the LPV-MPC control method are as follows: Figure 7 As shown. The controller output based on the LPV-MPC control method is as follows. Figure 8 As shown.
[0078] The experimental results show that the nonlinear compensation and control method for motion platforms provided by this invention can successfully capture the dynamic nonlinear dependence characteristics that traditional models cannot describe, and the model can maintain extremely high accuracy at different frequencies such as 20Hz, 60Hz, and 100Hz.
[0079] Furthermore, the prediction accuracy of the LPV model provided in this implementation far exceeds that of the traditional PI and RDPI models. For example, under random sinusoidal signal testing, the RMSE of the LPV model is 0.00069 μm, which is three orders of magnitude lower than the 2.11541 μm of the PI model, demonstrating significantly superior accuracy and generalization ability.
[0080] The nonlinear compensation and control system for a motion platform provided by the present invention will be described below. The nonlinear compensation and control system for a motion platform described below can be referred to in correspondence with the nonlinear compensation and control method for a motion platform described above.
[0081] Figure 9 This is a schematic diagram of the nonlinear compensation and control system for a motion platform provided by the present invention, as shown below. Figure 9 The device includes: The system model acquisition module 910 is used to acquire the linear variable parameter system model of the motion platform; wherein, the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; The system matrix determination module 920 is used to calculate the system matrix of the linear variable parameter system model at the current moment in real time based on the measured value of the scheduling variable of the motion platform, so as to obtain the linear system model at the current moment; The control input acquisition module 930 is used to determine the predicted output sequence of the motion platform through the linear system model at the current moment, so as to calculate the control input of the motion platform based on the predicted output sequence.
[0082] Based on any of the above embodiments, the system model acquisition module 910 is used to collect at least two sets of historical data of the motion platform; the historical data includes historical inputs, historical outputs and historical values of scheduling variables; and the system matrix of the linear variable parameter system model is identified and obtained based on the historical inputs, the historical outputs and the historical values of the scheduling variables.
[0083] Based on any of the above embodiments, the control input acquisition module 930 is used to input the historical output into the linear system model at the current moment to obtain the predicted output sequence of the motion platform output by the linear system model at the current moment.
[0084] Based on any of the above embodiments, a scheduling variable determination module is further included, which is used to determine the corresponding scheduling variable based on the nonlinear error of the motion platform before collecting at least two sets of historical data of the motion platform; wherein, the scheduling variable is a physical quantity that can be measured in real time.
[0085] Based on any of the above embodiments, the control input acquisition module 930 is used to determine the control input of the motion platform according to the predicted output sequence and the control target of the motion platform.
[0086] Based on any of the above embodiments, the control input acquisition module 930 is used to determine the control input sequence of the motion platform according to the predicted output sequence and the control target of the motion platform; and to determine the first input in the control input sequence as the control input of the motion platform.
[0087] Figure 10 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 10 As shown, the electronic device may include a processor 1010, a communications interface 1020, a memory 1030, and a communication bus 1040, wherein the processor 1010, communications interface 1020, and memory 1030 communicate with each other via the communication bus 1040. The processor 1010 can call logic instructions in the memory 1030 to execute a nonlinear compensation and control method for a motion platform. This method includes: acquiring a linear variable parameter system model of the motion platform; wherein the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; calculating the system matrix of the linear variable parameter system model at the current time based on the measured values of the scheduling variables of the motion platform in real time, obtaining the linear system model at the current time; determining the predicted output sequence of the motion platform through the linear system model at the current time, and calculating the control input of the motion platform based on the predicted output sequence.
[0088] Furthermore, the logical instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0089] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the nonlinear compensation and control method for a motion platform provided by the above methods. The method includes: obtaining a linear variable parameter system model of the motion platform; wherein the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; calculating the system matrix of the linear variable parameter system model at the current time in real time based on the measured values of the scheduling variables of the motion platform to obtain the linear system model at the current time; determining the predicted output sequence of the motion platform through the linear system model at the current time, and calculating the control input of the motion platform based on the predicted output sequence.
[0090] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a nonlinear compensation and control method for a motion platform provided by the methods described above. The method includes: acquiring a linear variable parameter system model of the motion platform; wherein the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; calculating the system matrix of the linear variable parameter system model at the current time in real time based on the measured values of the scheduling variables of the motion platform, thereby obtaining a linear system model at the current time; determining a predicted output sequence of the motion platform using the linear system model at the current time, and calculating the control input of the motion platform based on the predicted output sequence.
[0091] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A nonlinear compensation and control method for a motion platform, characterized in that, include: Obtain the linear variable parameter system model of the motion platform; wherein, the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; The system matrix of the linear variable parameter system model at the current moment is calculated in real time based on the measured value of the scheduling variable of the motion platform, so as to obtain the linear system model at the current moment. The predicted output sequence of the motion platform is determined by the linear system model at the current moment, and the control input of the motion platform is calculated based on the predicted output sequence.
2. The nonlinear compensation and control method for a motion platform according to claim 1, characterized in that, The process of obtaining the linear variable parameter system model of the motion platform includes: Collect at least two sets of historical data from the motion platform; the historical data includes historical inputs, historical outputs, and historical values of scheduling variables; The system matrix of the linear variable parameter system model is obtained by identifying the historical input, the historical output, and the historical values of the scheduling variables.
3. The nonlinear compensation and control method for a motion platform according to claim 2, characterized in that, The step of determining the predicted output sequence of the motion platform using the linear system model at the current moment includes: The historical output is input into the linear system model at the current moment to obtain the predicted output sequence of the motion platform output by the linear system model at the current moment.
4. The nonlinear compensation and control method for a motion platform according to claim 2, characterized in that, Before collecting at least two sets of historical data from the motion platform, the method further includes: The corresponding scheduling variables are determined based on the nonlinear error of the motion platform; wherein, the scheduling variables are physical quantities that can be measured in real time.
5. The nonlinear compensation and control method for a motion platform according to claim 1, characterized in that, The step of calculating the control input of the motion platform based on the predicted output sequence includes: The control input of the motion platform is determined based on the predicted output sequence and the control target of the motion platform.
6. The nonlinear compensation and control method for a motion platform according to claim 5, characterized in that, Determining the control input of the motion platform based on the predicted output sequence and the control target of the motion platform includes: The control input sequence of the motion platform is determined based on the predicted output sequence and the control target of the motion platform; The first input in the control input sequence is determined as the control input of the motion platform.
7. A nonlinear compensation and control system for a motion platform, characterized in that, include: The system model acquisition module is used to acquire the linear variable parameter system model of the motion platform; wherein, the system matrix of the linear variable parameter system model is a function of the scheduling variables of the motion platform; The system matrix determination module is used to calculate the system matrix of the linear variable parameter system model at the current moment in real time based on the measured value of the scheduling variable of the motion platform, so as to obtain the linear system model at the current moment; The control input acquisition module is used to determine the predicted output sequence of the motion platform through the linear system model at the current moment, so as to calculate the control input of the motion platform based on the predicted output sequence.
8. An electronic device comprising a memory, an actuator, a sensor, and a computer program stored in the memory and running on the processor, characterized in that, When the actuator and the sensor execute the computer program, they implement the nonlinear compensation and control method for a motion platform as described in any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the nonlinear compensation and control method for a motion platform as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the nonlinear compensation and control method for a motion platform as described in any one of claims 1 to 6.