Fuzzy Adaptive Control Of Permanent Magnet Synchronous Motor Stochastic System Based On State Constraint

Permanent magnet synchronous motor(PMSM)is widely used in new energy vehicles,robots,aerospace and other fields because of its wide speed regulation range,high power density and high efficiency.However,PMSM is a complex nonlinear control object with strong coupling,multivariable and easy to be affected by load disturbance,motor parameter change and other factors.Traditional control strategies are difficult to solve these problems and achieve high-performance control.In addition,PMSM will be disturbed by stochastic disturbance in the actual working scene.The existence of stochastic disturbance reduce the control accuracy,and even cause system oscillation or instability.At the same time,due to physical limitations,performance and safety factors,the state variables of PMSM,such as rotor angular speed and stator current,are often required to be constrained within a safe and reasonable range.Therefore,it is a very meaningful research topic to realize the position tracking control of PMSM drive system considering stochastic disturbance on the premise of ensuring state constraints.The research results of this paper are as follows:1.For the PMSM drive system considering stochastic disturbance,an adaptive backstepping control method based on fuzzy approximation and logarithmic barrier Lyapunov function(BLF)is proposed to realize its position tracking control.The logarithmic BLF is introduced to constrain the state variables such as speed and current,and the unknown nonlinear functions in the PMSM stochastic system model are processed by using the approximation characteristics of fuzzy logic system and adaptive technology.Finally,the boundedness of all signals in the closed-loop system is proved and the full state constraints are guaranteed.From the simulation results,it can be seen that the proposed control method can overcome the influence of stochastic disturbance and achieve good control effect,and all state variables of the system are constrained within a certain range.2.Aiming at the problem of computational complexity in the process of constructing control law with traditional backstepping,an adaptive command filtered backstepping method based on logarithmic BLF and fuzzy approximation is proposed to control the PMSM stochastic system with full state constraints.In the process of constructing the controller with the backstepping method,the derivative of the virtual control function is directly obtained by using the command filtered technology without tedious derivation operation,so as to solve the problem of computational complexity,and add the error compensation signal to ensure the control accuracy.Combined with logarithmic BLF and fuzzy adaptive technology,the effective control of PMSM stochastic system is realized,and the full state constraints are guaranteed.Through simulation and comparison experiments,it is verified that the command filtered technology has better control performance compared with the dynamic surface control technology because the error compensation signal is introduced to reduce the impact of filtering error on the system.3.For PMSM stochastic system without speed sensor,a fuzzy adaptive control strategy based on reduced order observer and logarithmic BLF is proposed.Further considering the situation that the sensor cannot be installed due to cost,environment and other factors.a reduced order observer is constructed to estimate the rotor angular velocity,which reduces the system investment and later maintenance cost,and reduces the hardware complexity.Combined with quartic logarithmic BLF and fuzzy adaptive technology,the effective tracking of the desired position signal is realized and the full state constraints are guaranteed.