Finite-time Command Filter Control Of Permanent Magnet Synchronous Motor Considering State Constraint

Permanent magnet synchronous motor(PMSM)is widely used in aerospace,electronic manufacturing,robotics and other fields due to its advantages of high power density,small size and high reliability.However,PMSM is a complex controlled object with characteristics such as multivariable,strong coupling,and high-order nonlinearity,and is susceptible to load disturbances and external uncontrollable environmental factors.Therefore,traditional control strategies are difficult to achieve high-quality control of PMSM.In addition,physical constraints are common in practical engineering.All state variables such as rotor angular velocity and stator current of PMSM need to be constrained within the allowable safety range,otherwise the motor will encounter safety and performance issues.At the same time,modern industry has put forward higher requirements for the accuracy,efficiency,and anti-interference of controlled systems,and there are currently few achievements in researching advanced control strategies to achieve fast and efficient tracking control of motors.In summary,studying high-precision position tracking control strategies and improving convergence speed and anti-interference performance for PMSM systems with full state constraints has theoretical guiding significance and engineering application value.This dissertation focuses on the high-performance control requirements of PMSM and the finite-time tracking control problem of PMSM with full-state constraints is studied by combining the command filtered backstepping method,finite-time control(FTC),adaptive neural network and barrier Lyapunov function.The main research contents are as follows:(1)For a class of strict-feedback nonlinear time-varying state-constrained systems,a finite-time command filtered tracking control problem based on neural network approximation is studied.Firstly,the controller designed by using the logarithmic asymmetric time-varying barrier Lyapunov Function can solve the time-varying full-state constraint problem of nonlinear systems.Then,the FTC technology is introduced to make the controlled system have a faster convergence rate than the asymptotic convergence.In addition,the command filtered technology solves the inherent "explosion of complexity" problem of traditional backstepping method,and an improved finite-time error compensation mechanism is designed to offset the negative impact of filter error on system performance.(2)On the basis of research content one,the adaptive neural network finite-time position tracking control problem is further studied for PMSM systems considering constant full-state constraints.Firstly,based on the command filtered backstepping design method,a logarithmic constant barrier Lyapunov Function is introduced to ensure that the angular velocity,stator current and other state variables of PMSM system are constrained within a predefined ranges.At the same time,FTC technology is combined to accelerate the convergence of the system and improve the anti-interference ability for load torque.Finally,the simulation and comparison experiments verify that the finite-time command filtered constraint control strategy has the advantages of faster convergence speed and higher tracking accuracy and all states are always constrained in the preset range compared with traditional backstepping.(3)Considering the time-varying requirements for the system state boundary range in actual production control,the control problem of asymmetric time-varying full state constraints for PMSM was further studied.An asymmetric time-varying barrier Lyapunov Function that is obviously dependent on time is adopted to prevent the whole state quantity from violating the time-varying constraint,allowing the constraint boundary to change with the expected trajectory of time.Combined with FTC,command filtered technology and adaptive neural network technology,the designed controller can ensure the stability of the PMSM system without violating the time-varying full state constraints.The effectiveness of the control algorithm is verified through simulation and experimental platforms.The results show that the control scheme can achieve good fast tracking effect and all states are always maintained within the preset range.