Optimal Design Of Iterative Learning Control Strategy For Suppressing PMSM Torque Ripple

Permanent magnet synchronous motor has the advantages of high control precision and high reliability;and is widely used in industrial and agricultural production,new energy vehicles,and other fields.However,due to the inherent nonideal factors in the motor drive system,there are periodic torque ripples in the output torque,which limits its development in high-precision application scenarios.The iterative learning control strategy is widely used in motor control because of its excellent inhibition ability to periodic torque ripple.However,the strategy has some problems,such as a fixed forgetting factor and a limited adjustment range of learning rate parameters,which limit its ability to suppress torque ripple.In this paper,the permanent magnet synchronous motor is the research object,the iterative learning control strategy optimization design,so as to improve the motor torque ripple suppression ability.The main research work and contributions of this paper are as follows:Firstly,the coordinate transformation principle of permanent magnet synchronous motor is introduced,and the mathematical model in the d-q coordinate system is constructed.The vector control of a permanent magnet synchronous motor is realized by using vector pulse width modulation technology.The vector control system of a permanent magnet synchronous motor is built and the torque ripple characteristics of the motor are analyzed.Secondly,the iterative learning control strategy is expounded and an open-closed loop iterative learning controller is designed.Compared with the traditional PI controller,the simulation results show that the iterative learning control strategy has better performance in suppressing periodic torque ripple.Then,in order to solve the problem of valuable signal loss caused by the constant value of the forgetting factor parameter of iterative learning,this paper obtains the expression between the optimal gains of the system according to the optimization theory,combines the motor control model for parameter optimization,and designs an adaptive parameter iterative learning The simulation results show that the controller has the ability to adjust the forgetting factor online,and has a better effect on torque ripple suppression.Then,aiming at the limited selection range of learning rate parameters for iterative learning,this paper introduces the fractional order calculus theory into the PID type iterative learning controller;and designs a fractional order PID type iterative learning controller,thereby expanding the parameter setting range.Improve torque ripple suppression capability.But at the same time,the introduction of fractional order increases the number of variable learning rate parameters from 3 to 5,making parameter tuning more difficult.Aiming at this problem,fuzzy control theory is introduced to design a fuzzy fractional-order PID iterative learning controller,thereby optimizing five variable parameters in the designed fractional-order PID iterative learning controller.The simulation results show that the controller can not only It can effectively reduce torque fluctuations,and can also adjust the learning rate of iterative learning online in real-time to improve the dynamic and static performance of the control system.Finally,a hardware experiment platform based on Speedgoat semi-physical simulation system was constructed,and the proposed adaptive parameter iterative learning control strategy and fuzzy fractional PID iterative learning control strategy proposed in this paper are experimentally verified through the hardware-in-the-loop test on the experimental platform.Experimental results show that the optimized design scheme of the learning control strategy proposed in this paper has a better torque ripple suppression effect.