Finite-time Fuzzy Adaptive Control Of Permanent Magnet Synchronous Motor Based On State Constraints

Due to the advantages of high efficiency and high torque inertia ratio,Permanent magnet synchronous motors(PMSMs)have attracted a great deal of attention in varieties of applications,including vehicle,robot and aerospace.PMSMs have the characteristics of multivariable and strong coupling,and are easily affected by uncertain factors such as motor parameter variation and external torque disturbance in the actual industrial application.Therefore,it is difficult for traditional control methods to obtain better control performance in the transient response stage.In addition,PMSMs rotor angular velocity and stator current and other state variables should be operated within safe constraints.If the state variables exceed the constraint range,it may affect the performance of PMSMs,and even cause motor failure and safety problems.At the same time,in order to achieve the effective control of PMSMs,it is very important to complete the tracking task of the motor system in a shorter time.Therefore,it is a significant research direction to realize fast and efficient tracking control for PMSMs with state constraints.In this dissertation,the position tracking control of PMSMs servo system is studied by combining backstepping,barrier Lyapunov function(BLF),finite-time control(FTC)and dynamic surface/command filtering techniques.The main research results are as follows:1.For a class of strict feedback nonlinear constraint systems,a new fuzzy adaptive FTC strategy based on logarithmic BLF and dynamic surface control is studied.By using the characteristics of logarithmic BLF,the tracking effect is guaranteed without violating the state constraints.In addition,by combining the FTC theory,the convergence speed of the controller is improved,and the dynamic surface control is used to solve the disadvantage of "computational expansion" in the backstepping method.2.For PMSMs position tracking system,an adaptive fuzzy finite-time dynamic surface control method based on full-state constraints is proposed.By combining the FTC method with the logarithmic BLF,the angular velocity,stator current and other state variables of PMSMs are constrained on a predefined compact set,and the finite-time tracking control of the motor system is realized.In addition,dynamic surface control is used to solve the problem of "computational expansion" in backstepping,and fuzzy logic system is used to approximate nonlinear functions.Finally,the fast tracking effect of the control method and the boundedness of all signals are proved;The simulation results show the superiority of the new design.3.A fuzzy adaptive backstepping control strategy based on BLF for speed sensorless PMSMs with full-state constraints is studied.Firstly,a fuzzy reduced order-observer is established to estimate the rotor angular velocity of PMSMs,and the adaptive fuzzy technique is used to deal with the problems of unknown parameters and load disturbance.Secondly,the logarithmic BLF is used to ensure the tracking effect without violating the state constraints.In addition,in order to solve the problem of "calculation expansion" in the conventional backstepping method,the command filtering technology is used to filter the virtual control signal to get the approximate value of its derivative.Furthermore,the error compensation mechanism is combined to eliminate the adverse effect of the accumulated filtering error on the control performance.