Networked motion control system position tracking control method based on high-order all-drive

Abstract

The invention discloses a networked motion control system position tracking control method based on high-order all-drive, and the method employs a high-order all-drive theory to convert a system state space model into a second-order all-drive model with dead zone nonlinearity, thereby providing great convenience for the design of a controller. A dead zone pre-compensation method is adopted to compensate a deterministic parameter part of a dead zone in the system identification process, the system is helped to reduce response delay, and the dynamic performance is improved; an equivalent input interference method is adopted to integrate internal disturbance and external disturbance into input channel interference, so that the design complexity of the control system is reduced; an intermediate observer is introduced to estimate the interference of an input channel, and the accuracy and convergence speed of disturbance estimation are improved by adjusting parameters of the observer; and designing an MPC controller to solve a control law, and inversely transforming the control law into an all-drive control law through a corresponding relational expression to realize high-precision control of the system.

Description

Technical Field

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

[0002] In recent decades, with the rapid development of industrial control technology, networked motion control systems have become a hot topic in academia and industry, and have been widely used in fields such as mechanical manufacturing, unmanned vehicles, and robotic manipulators. Networked motion control systems have three control modes, namely position, speed, and torque. Among them, the speed mode can directly realize speed control or speed-position control, and has received great attention in occasions that require precise control, such as extended-range electric vehicle engines, doubly fed wind turbines, ship diesel engines, and multi-degree-of-freedom motion motors of spacecraft. However, in the actual speed-position control process, due to the mechanical and physical characteristics of the system, its control process exhibits nonlinear characteristics such as dead zone, saturation, and gap. In addition, due to inaccurate system identification results, system aging and wear, and environmental noise, networked motion control systems are also affected by system uncertainty and external interference. Therefore, high-precision position tracking control of networked motion control systems with dead zone nonlinearity, system uncertainty, and external interference has always been an urgent problem to be solved.

[0003] In order to solve the disturbance suppression control problem of networked motion control systems in speed mode, 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 observer 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 position tracking 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 position tracking control method for a networked motion control system based on a high-order all-wheel drive. First, for a networked motion control system in a speed mode, a speed excitation signal is given to collect its position output signal; secondly, based on the collected speed input signal and position output signal, a second-order state space model with dead zone nonlinearity is obtained using the Matlab system identification toolbox, which is further discretized and converted into a high-order all-wheel drive model; then, for the deterministic parameter part in the dead zone nonlinear characteristics of the system model, a dead zone pre-compensation method is used to compensate; then, for the uncertain parameter part in the dead zone and the system, it is abstracted as an internal disturbance and combined with the external disturbance as an equivalent input disturbance, and an intermediate observer is designed to estimate and compensate for the impact of the disturbance; finally, a high-precision tracking controller is designed using the MPC method.

[0007] The high-order all-wheel drive theory is used to transform the system state space model into a second-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 deterministic parameter part of the dead zone during 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.

[0008] In order to achieve the above purpose, the technical solution of this application is as follows: A position tracking control method for a networked motion control system based on a high-order all-wheel drive, comprising: For the networked motion control system in the speed mode, the method of given speed excitation signal collects its position output signal; According to the speed input signal and position output signal, the Matlab system identification toolbox is used to obtain a second-order state space model with dead zone nonlinearity. Considering the uncertainty of the identification parameters, the state equation of the system is as follows:

[0009] (1); in, , Indicates the system status. Indicates the system input speed signal, Indicates the system output position signal, represents the external disturbance signal, Indicates the dead-band input signal. Indicates system parameters, , Represents the slope of the left and right dead zone characteristics, , Indicates the deadband characteristic breakpoints on the left and right axes. represents the system parameter uncertainty, represents the dead zone parameter uncertainty.

[0010] right Derivation and Substitute, according to the sampling time After discretization, we can get the following state space equation: (2); in, , , , From (1) and (2), it can be seen that the dead zone of the system consists of a deterministic parameter part and an uncertain parameter part. For the deterministic parameter part, the dead zone pre-compensation method is used to compensate, and the definition is There are the following forms: (3); in, Represents the control input calculated by the upper-level controller.

[0011] According to (2)-(3), the relationship between the control input calculated by the upper controller and the system input speed can be obtained: (4); Substituting (4) into (2) and abstracting the dead zone and system uncertainty parameters as internal disturbances of the system, the following system state space equation can be obtained: (5); (6); 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: (7); in is a matrix that can be arbitrarily formulated. An external signal.

[0012] Substitute (7) into (5) to convert the original system into all-wheel drive form (8); Based on the idea of ​​equivalent input disturbance, internal disturbance and external disturbance are integrated into input channel disturbance , we can get: (9); remember , for input channel interference, an intermediate observer is constructed for estimation. The intermediate variable is , the observer is designed as follows: (10); 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: (11); remember , , , ,have: (12); remember , , , ,have: (13); 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: (14); 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 .

[0013] For a given reference speed signal, a model predictive controller is designed for the high-order all-wheel drive speed model of the networked motion control system. Considering the fault-free case, there is , then the state equation of the system is: (15); Setting prediction intervals , the recursive expression is as follows: (16); The discrete reference signal is denoted as , define the quadratic performance index as: (17); in, Represents the system's operating cost weight matrix, that is, the cost of the system's adjustment state deviation; Represents the control cost weight matrix of the system, that is, the cost of the control input. 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 to complete high-precision position tracking control; Compared with the prior art, the advantages of this application are: 1) The full drive system model is used to describe the networked 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.

[0014] 2) The dead zone uncertainty is divided into two parts: the deterministic parameter part and the uncertain parameter part. For the deterministic parameter part, dead zone pre-compensation is performed, while for the uncertain parameter part, an intermediate observer is designed based on the all-wheel drive system model to achieve accurate estimation.

[0015] 3) An MPC method based on a high-order all-wheel drive model is proposed, and the MPC controller and the intermediate observer are combined to achieve high-precision position tracking control of the networked motion control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the position tracking control method of the networked motion control system based on high-order all-wheel drive in this application; Figure 2 is a tracking curve of the state variable to the reference signal in this application; Figure 3 This is a comparison curve of the control law before and after dead zone pre-compensation in this application. DETAILED DESCRIPTION

[0017] 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.

[0018] In one embodiment of the present application, the steps are as follows Figure 1 As shown, a speed control method of a networked motion control system based on a high-order all-wheel drive is proposed, including the following steps: Step S1: 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: S1.1) For the networked motion control system in the speed mode, the method of given speed excitation signal is used to collect its position output signal; according to the speed input signal and the position output signal, the Matlab system identification toolbox is used to obtain the second-order state space model with dead zone nonlinearity. At the same time, considering the uncertainty of the system identification parameters, the state equation of the system is as follows: (1); in, , Indicates the system status. Indicates the system input speed signal, Indicates the system output position signal, represents the external disturbance signal, Indicates the dead-band input signal. Indicates system parameters, , Represents the slope of the left and right dead zone characteristics, , Indicates the deadband characteristic breakpoints on the left and right axes. represents the system parameter uncertainty, represents the dead zone parameter uncertainty, and all parameter uncertainties are in the following form: ; right Derivation and Substitute and press the sampling time After discretization, we can get the following state space equation: (2); in , represents the parameter matrix, represents the sampling time, From (1) and (2), it can be seen that the dead zone of the system consists of a deterministic parameter part and an uncertain parameter part. For the deterministic parameter part, the dead zone pre-compensation method is used to compensate, and the definition is There are the following forms: (3); in, Represents the control input calculated by the upper-level controller.

[0019] S1.2) According to (1)-(2), the relationship between the control input calculated by the upper controller and the system input speed can be obtained: (4); Substituting (4) into (2) and abstracting the uncertainty parameter part as the internal disturbance of the system, we can get the following system state space: (5); (6); 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: (7); in , An external signal.

[0020] Substitute (7) into (5) to convert the original system into all-wheel drive form (8); Step S2, constructing an intermediate observer, designing and solving the intermediate observer gain through matrix inequality, and realizing the estimation of equivalent input interference, including the following steps; S2.1) Based on the idea of ​​equivalent input disturbance, internal disturbance and external disturbance are integrated into input channel disturbance , we can get; (9); remember , , for input channel interference, an intermediate observer is constructed for estimation. The intermediate variable is , the observer is designed as follows: (10); 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.

[0021] S2.2) Constructing error model and ,have: (11); remember , , , ,have: (12); remember , , , ,have: (13); S2.3) Construct the Lyapunov function to analyze the stability of the error system, let , construct the stability matrix and solve it to obtain the intermediate observer gain: (14); Solving the above linear matrix inequality, we can get , , , and the state of the error system is uniformly eventually bounded.

[0022] Step S3, designing an MPC controller to solve the optimal control sequence, and obtaining the final input of the networked motion control system through inverse transformation, including the following steps: S3.1) For a given reference speed signal, design a model predictive controller for a high-order system, considering the fault-free case, , then the state equation of the system is: (15); Setting prediction intervals , the recursive expression is as follows: (16); in represents the matrix transpose symbol,

[0023]

[0024] The discrete reference signal is denoted as , define the quadratic performance index as: (17); in, Represents the system's operating cost weight matrix, that is, the cost of the system's adjustment state deviation; Represents the control cost weight matrix of the system, that is, the cost of the control input. 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 , and the solution is It will first be processed by dead zone pre-compensation, and then input into the networked motion control system to compensate for the influence of the system dead zone. The control law comparison curve before and after dead zone pre-compensation is as follows: Figure 3 As shown in . Under this control law, the system state can track the reference signal very well, and the tracking curve of its state variable to the reference signal is as follows Figure 2 As shown, high-precision position tracking control of the networked motion control system is achieved.

[0025] 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 position tracking control method for a networked motion control system based on a high-order all-wheel drive, characterized in that: The steps include: (1) For the motion control system in the speed mode, 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 networked motion control system in the speed mode, the speed excitation signal is given to collect its position output signal; secondly, according to the collected speed input signal and position output signal, the Matlab system identification toolbox is used to obtain the second-order state space model with dead zone nonlinearity, which is discretized and converted into a high-order all-wheel drive model; then, the dead zone pre-compensation method is used to compensate for the deterministic parameter part of the dead zone nonlinear characteristics of the system model; (2) Construct an intermediate observer, design and solve the intermediate observer gain through matrix inequality to estimate the equivalent input disturbance. Specifically, for the dead zone and the uncertainty parameter part in the system, abstract them into internal disturbances and combine them with external disturbances into equivalent input disturbances. Design an intermediate observer to estimate and compensate for the impact of the disturbance. (3) The MPC method is used to design a high-precision tracking controller. The MPC controller is designed to solve the optimal control sequence and the final input of the networked motion control system is obtained through inverse transformation.

2. The position tracking control method of a networked motion control system based on a high-order all-wheel drive according to claim 1 is characterized in that: The step (1) uses the high-order all-wheel drive theory to convert the system state space model into a second-order all-wheel drive model with dead zone nonlinearity, which is expressed by the following formula: ;in, Indicates the system status. Indicates the system input speed signal, Indicates the system output position signal, represents the external disturbance signal, Indicates the dead zone input signal, Indicates the sampling time; represents the parameter matrix; represents the deterministic part of the system identification parameters, Indicates the uncertainty part; , Indicates the slope of the left and right dead zone characteristics, , Indicates the dead zone characteristic breakpoints on the left and right axes, Represents the dead zone uncertainty parameter.

3. The position tracking control method of a networked motion control system based on a high-order all-wheel drive according to claim 1, characterized in that: The dead zone pre-compensation method described in step (1) is used to compensate for the deterministic parameter part 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 position tracking control method of a networked motion control system based on a high-order all-wheel drive according to claim 1, characterized in that: The equivalent input interference described in step (2) is expressed as follows: ； in, For external disturbance and internal disturbances The integrated input channel interference.

5. The position tracking 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 is 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.

6. The position tracking 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 (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, that is, to save costs while meeting the system performance requirements; according to , the MPC solution The inverse transformation is After dead zone pre-compensation, it is brought into the networked motion control system to complete high-precision position tracking control.

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