Moment mode control method of networked motion control system based on high-order all-drive

Abstract

The invention discloses a high-order all-drive-based networked motion control system moment mode control method, which comprises the following steps of (1) obtaining a state space model of a system by adopting a Matlab system identification toolbox, performing pre-compensation on a dead zone, designing an all-drive control law, and converting the state space model into an all-drive model; (2) constructing an intermediate observer, and designing and solving the gain of the intermediate observer through a matrix inequality; and (3) designing an MPC controller to solve the optimal control sequence, and obtaining the final input of the networked motion control system through inverse transformation. Compared with the prior art, the method has the advantages that an all-drive system model is used for describing a moment mode motion control system with parameter uncertainty, dead drive characteristics and external disturbance, the physical significance of the system state is kept, and controller design is facilitated; and the MPC controller and the intermediate estimator are combined, so that high-precision disturbance suppression control of the moment mode of the networked motion control system is realized.

Description

Technical Field

[0001] The present application belongs to the technical field of networked motion control, and specifically relates to a torque mode control method of a networked motion control system based on a high-order all-wheel drive. Background Art

[0002] With the rapid development of industrial Internet and network communication technology, networked motion control systems have become a hot topic in academia and industry, and are widely used in machine tool manufacturing, unmanned vehicles, microgrids and other fields. It overcomes the problems of complex wiring, poor scalability and difficult maintenance in traditional motion control systems. However, networking also brings uncertainty to the control system, such as delay, packet loss, etc., and does not solve the influence of the time-varying parameters, dead zone characteristics, external disturbances and other uncertain factors of the motion control system on the control performance. Therefore, for networked motion control systems with uncertain factors, achieving high-precision disturbance suppression control has become an important research focus and has received widespread attention.

[0003] In order to solve the disturbance rejection control problem in networked motion control systems, the observer method has been widely studied. The observer method regards uncertainty and external disturbances as integrated disturbances. Then, an observer is designed to obtain an estimate of the integrated disturbance and offset the disturbance effect in a feedforward manner. Among them, the intermediate estimator does not need to meet the observer matching condition, and the accuracy and convergence speed of the disturbance estimation can be improved by adjusting the observer parameters. Therefore, it is widely used in networked motion control systems.

[0004] After obtaining accurate disturbance estimation information, it is necessary to design a high-performance disturbance suppression controller to achieve high-precision disturbance suppression control of networked motion control systems. To this end, researchers have extensively studied advanced methods such as sliding mode control, robust control, and model predictive control (MPC).

[0005] It is worth noting that most of the above methods still use state-space models to describe networked control motion systems, which leads to the loss of the physical meaning of the original system and the emergence of ill-conditioned matrices in the process of model simplification. Compared with the state-space model, the all-wheel drive system is directly established from the laws of physics, which can more naturally represent the real system and provides great convenience. Summary of the invention

[0006] The purpose of this application is to provide a torque mode control method for a networked motion control system based on a high-order all-wheel drive. First, for a motion control system in torque mode, a torque excitation signal is given to collect its speed output signal; secondly, based on the collected torque input signal and speed output signal, a first-order state space model with dead zone nonlinearity is obtained and discretized using the Matlab system identification toolbox; then, in view of the dead zone nonlinear characteristics of the system model and the uncertainty of the identification parameters, a dead zone pre-compensation method is used to compensate; then, an intermediate observer is designed to estimate the system disturbance and compensate for the impact of the disturbance; finally, a high-performance disturbance suppression controller is designed using the MPC method to achieve high-precision error suppression control.

[0007] In order to achieve the above purpose, the technical solution of this application is as follows: A torque control method for a networked motion control system based on a high-order all-wheel drive, comprising: For the motion control system in torque mode, the speed output signal is collected by a method of given torque excitation signal; According to the torque input signal and the speed output signal, the Matlab system identification toolbox is used to obtain the first-order state space model with dead zone nonlinearity and discretize it. Considering the uncertainty of the system identification parameters, the state equation of the system is as follows:

[0008] (1); in, Indicates the system status. Represents the system input torque signal, Indicates that the system outputs a speed signal. represents the external disturbance signal, Indicates the dead-band input signal. represents the parameter matrix. and Represents the slope of the left and right dead zone characteristics, and Indicates the deadband characteristic breakpoints on the left and right axes. and Indicates the uncertain parameters in the dead zone parameters.

[0009] The dead zone characteristics are divided into the intrinsic uncertainty part composed of deterministic parameters and the unknown uncertainty part composed of uncertain parameters. For the intrinsic uncertainty, the dead zone pre-compensation method is used to compensate, and the definition is There are the following forms: (2); in, Represents the control input calculated by the upper-level controller.

[0010] According to (1)-(2), the relationship between the control input calculated by the upper-level controller and the system input torque can be obtained: (3); Substituting (3) into (1) and abstracting the unknown uncertainty part into the internal disturbance of the system, we can obtain the following system state space equation: (4); Based on the high-order full-drive theory, if the coefficient matrix Reversible, the above system is all-wheel drive. Use the direct parameter method to design the all-wheel drive control law, convert the state space model into an all-wheel drive model, and design There are the following forms: (5); in is a matrix that can be arbitrarily formulated. An external signal.

[0011] Substitute (5) into (4) to convert the original system to full-wheel drive. (6); Based on the idea of ​​equivalent input disturbance, internal disturbance and external disturbance are integrated into input channel disturbance ; (7); remember , , for input channel interference, an intermediate observer is constructed for estimation. The intermediate variable is , the observer is designed as follows: (8); in, is the control law obtained by the MPC controller, which is used to offset the equivalent input disturbance of the system. is a suitable scalar variable, is the observer gain matrix to be determined. Construct error model and ,have: (9); remember , , , ,have: (10); remember , , , ,have: (11); Construct Lyapunov function to analyze the stability of the error system, let , , is a matrix variable of appropriate dimension. Construct the stability matrix and solve it to obtain the intermediate observer gain: (12); If the above linear matrix inequality has a solution, then the state of the error system is uniformly bounded, and the observer gain can be obtained .

[0012] For a given reference speed signal, a model predictive controller is designed for the high-order full-drive torque model of the networked motion control system. Considering the fault-free case, there is , then the state equation of the system is: (13); Setting prediction intervals , the recursive expression is as follows: (14); The discrete reference signal is , define the quadratic performance index as: (15); in, Represents the system's operating cost weight matrix, that is, the cost of the system's adjustment state deviation; The cost weight matrix represents the control quantity of the system, that is, the cost of the control input. MPC control aims to find the balance point of the two parts, that is, to save costs while meeting the system performance requirements.

[0013] The optimization objective function is solved by the Yalmip toolbox to obtain the optimal control sequence . Take the first element of the sequence as the system control law at the current moment, and according to , the MPC solution The inverse transformation is , after dead zone pre-compensation, it is used as the input of the networked motion control system, thereby completing the high-precision disturbance suppression control of the torque mode.

[0014] Compared with the prior art, the advantages of this application are: 1) The full drive system model is used to describe the torque mode motion control system with parameter uncertainty, dead drive characteristics and external disturbances, which maintains the physical meaning of the system state and facilitates controller design.

[0015] 2) The dead zone uncertainty is divided into two parts: intrinsic and unknown. The intrinsic uncertainty is pre-compensated, and for the unknown part, an intermediate estimator is designed based on the all-wheel drive system model to achieve accurate estimation.

[0016] 3) An MPC method based on a high-order all-wheel drive model is proposed, and the MPC controller and the intermediate estimator are combined to achieve high-precision disturbance suppression control of the torque mode of the networked motion control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a framework diagram of the torque mode control method of the networked motion control system based on high-order all-wheel drive in this application; Figure 2 This is a flow chart of the torque mode control method of the networked motion control system based on high-order all-wheel drive in this application; Figure 3 is a tracking curve of the state variable to the reference signal in this application; Figure 4 This is a comparison curve of the control law before and after dead zone pre-compensation in this application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] In one embodiment of the present application, a torque control method of a networked motion control system based on a high-order all-wheel drive is proposed, comprising the following steps: Step 1: Use Matlab system identification toolbox to obtain the state space model of the system, pre-compensate the dead zone, and design the all-wheel drive control law to convert the state space model into an all-wheel drive model: 1.1) For the networked motion control system in torque mode, the speed output signal is collected by a given torque excitation signal; based on the torque input signal and the speed output signal, the Matlab system identification toolbox is used to obtain a first-order state space model with dead zone nonlinearity and discretize it. Considering the uncertainty of the system identification parameters, the state equation of the system is as follows:

[0020] (1); in, Indicates the system status. Represents the system input torque signal, Indicates that the system outputs a speed signal. represents the external disturbance signal, Indicates the dead zone input signal, represents the parameter matrix. and Represents the slope of the left and right dead zone characteristics, and Represents the dead zone characteristic breakpoints on the left and right axes. All parameter uncertainties are in the following form: ; The dead zone characteristics are divided into the intrinsic uncertainty part composed of deterministic parameters and the unknown uncertainty part composed of uncertain parameters. For the intrinsic uncertainty, the dead zone pre-compensation method is used to compensate, and the definition is In the following form: (2); in, Represents the control input calculated by the upper-level controller.

[0021] 1.2) According to (1)-(2), the relationship between the control input calculated by the upper controller and the system input torque can be obtained: (3); Substituting (3) into (1) and abstracting the unknown uncertainty part into the internal disturbance of the system, we can get the following system state space: (4); Based on the high-level all-wheel drive theory, here The above system is all-wheel drive. The direct parameter method is used to design the all-wheel drive control law. The state space model is converted into an all-wheel drive model. There are the following forms: (5); in , An external signal.

[0022] Substitute (5) into (4) to convert the original system to full-wheel drive. (6); Step 2: construct an intermediate observer, design and solve the intermediate observer gain through matrix inequality to estimate the equivalent input interference, including the following steps: 2.1) Based on the idea of ​​equivalent input disturbance, internal disturbance and external disturbance are integrated into input channel disturbance ; (7); in , , for input channel interference, an intermediate observer is constructed for estimation. The intermediate variable is , the observer is designed as follows: (8); in, is the control law obtained by the MPC controller, which is used to offset the equivalent input disturbance of the system. As the intermediate observer parameter, is the observer gain matrix to be solved.

[0023] 2.2) Building an error model and ,have: (9); remember , , , ,have: (10); remember , , , ,have: (11); 2.3) Construct Lyapunov function to analyze the stability of the error system, let , construct the stability matrix and solve it to obtain the intermediate observer gain: (12); Solving the above linear matrix inequality, we can get , , , and the state of the error system is uniformly eventually bounded.

[0024] Step 3: Design an MPC controller to solve the optimal control sequence and obtain the final input of the networked motion control system through inverse transformation, including the following steps: 3.1) For a given reference speed signal, a model predictive controller is designed for a high-order system. Considering the fault-free case, we have , then the state equation of the system is: (13); Setting prediction intervals , the recursive expression is as follows: (14); in represents the matrix transpose symbol, ; The discrete reference signal is , define the quadratic performance index as: (15); in, Represents the system's operating cost weight matrix, that is, the cost of the system's adjustment state deviation; The cost weight matrix represents the control quantity of the system, that is, the cost of the control input. MPC control aims to find the balance point of the two parts, that is, to save costs while meeting the system performance requirements.

[0025] The optimization objective function is solved by the Yalmip toolbox to obtain the optimal control sequence . Take the first element of the sequence as the system control law at the current moment, and according to , the MPC solution The inverse transformation is , after dead zone pre-compensation, it is used as the input of the networked motion control system, thereby completing the high-precision disturbance suppression control of the torque mode.

[0026] The high-order all-wheel drive theory is used to convert the system state space model into a first-order all-wheel drive model with dead zone nonlinearity, which greatly facilitates the design of the controller; the dead zone pre-compensation method is used to compensate for the intrinsic uncertainty of the dead zone in the system identification process, helping the system to reduce response delay and improve dynamic performance; the equivalent input disturbance method is used to integrate internal disturbances and external disturbances into input channel disturbances, reducing the complexity of control system design; an intermediate observer is introduced to estimate the input channel disturbance, and the accuracy and convergence speed of disturbance estimation are improved by adjusting the observer parameters; an MPC controller is designed to solve the control law and inversely transform it into the all-wheel drive control law through the corresponding relationship to achieve high-precision control of the system.

[0027] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A torque mode control method for a networked motion control system based on a high-order all-wheel drive, characterized in that: The steps include: (1) The Matlab system identification toolbox is used to obtain the state space model of the system, pre-compensate the dead zone, and design the all-wheel drive control law to convert the state space model into an all-wheel drive model; Firstly, for the motion control system in torque mode, a torque excitation signal is given to collect its speed output signal; Secondly, according to the acquired torque input signal and speed output signal, the first-order state space model with dead zone nonlinearity is obtained and discretized using Matlab system identification toolbox; Furthermore, the dead zone pre-compensation method is used to compensate for the nonlinear characteristics of the dead zone of the system model and the uncertainty of the identification parameters; (2) Construct an intermediate observer, design and solve the intermediate observer gain through matrix inequality to estimate the equivalent input disturbance: Design the intermediate observer to estimate the system disturbance and compensate for the impact of the disturbance; (3) Design an MPC controller to solve the optimal control sequence and obtain the final input of the networked motion control system through inverse transformation: Use the MPC method to design a high-performance disturbance suppression controller to achieve high-precision error suppression control.

2. The torque mode control method of a networked motion control system based on a high-order all-wheel drive according to claim 1 is characterized in that: In step (1), the system state space model is converted into a first-order all-wheel drive model with dead zone nonlinearity using high-order all-wheel drive theory, which is expressed as follows: ； in, Indicates the system status. Represents the system input torque signal, Indicates that the system outputs a speed signal. represents the external disturbance signal, Indicates the dead zone input signal; represents the parameter matrix; and Represents the slope of the left and right dead zone characteristics, and Indicates the dead zone characteristic breakpoints on the left and right axes; and Indicates the uncertain parameters in the dead zone parameters.

3. The torque mode control method of a networked motion control system based on a high-order all-wheel drive according to claim 1 is characterized in that: In step (1), the dead zone pre-compensation method is used to compensate for the intrinsic uncertainty of the dead zone during the system identification process, which is expressed by the following formula: ； in, represents the control input obtained by the upper controller, Represents the control input after dead zone pre-compensation.

4. The torque mode control method of a networked motion control system based on a high-order all-wheel drive according to claim 1 is characterized in that: In step (2), the equivalent input interference method is expressed by the following formula: ； in, For external disturbance and internal disturbances The integrated input channel interference.

5. The torque mode control method of a networked motion control system based on a high-order all-wheel drive according to claim 1, characterized in that: In step (2), an intermediate observer is introduced to estimate the input channel interference, which can be expressed as follows: ； in, is the control law obtained by the MPC controller, which is used to offset the equivalent input disturbance of the system. is a suitable scalar variable, is the observer gain matrix to be determined; ； in, represents the control input obtained by the upper controller, Represents the control input after dead zone pre-compensation.

6. The torque mode control method of a networked motion control system based on a high-order all-wheel drive according to claim 1 is characterized in that: In step (3), an MPC controller is designed to solve the control law and inversely transform it into the full-drive control law through the corresponding relationship, which is expressed as follows: ; Represents the parameter matrix of the system, which together constitutes a recursive equation with a prediction interval of 5; the discrete reference signal is , define the quadratic performance index as: ； in, Represents the system's operating cost weight matrix, that is, the cost of the system's adjustment state deviation; Represents the control quantity cost weight matrix of the system, that is, the cost of the control input. MPC control aims to find the balance point of the two parts; according to , the MPC solution The inverse transformation is , after dead zone pre-compensation, it is brought into the motion control system to complete high-precision control of the torque mode.

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