Optimized PID Parameter Tuning Of Flutter Plane Drive Motor Based On Pimproved Gray Wolf Algorithm

Raptor type flutter can be used for high altitude weather detection,ecological monitoring,agricultural irrigation,fire rescue and many other occasions.Its drive system mostly uses Permanent Magnet Synchronous Motor(PMSM).In order to meet its functional requirements,the civilian raptor flutter aircraft must have a long range and the ability to cope with complex weather changes,inevitably encounter air turbulence and turbulence and other complex conditions when flying in the air.Therefore,the control system of civil raptor flutter aircraft has a two-way requirement of real-time and stability.The traditional PID control algorithm,which has accumulated a large amount of human long-term experience data of stable operation and a wide range of control motor uses,is well suited for application to the steady-state flight process of civil raptor-type flutter aircraft.Meanwhile,the transient attitude adjustment for its capture process needs to be rectified in combination with a multi-target population intelligence algorithm to achieve its rapidity requirements.In this paper,a population intelligence algorithm,the Gray Wolf algorithm,is improved(IGWO)and combined with a conventional PID to address this type of problem.And the method is used in PMSM as a control system for flutter plane drive motor to improve its control quality.The following work has been done to achieve a high precision adjustment of the parameters of the permanent magnet synchronous motor.First,the coordinate transformation method allows the construction of a mathematical model of the permanent magnet synchronous motor,in which the mathematical model of the motor and its various management strategies are elaborated.Under the comparison,the vector control strategy with Id=0 is selected and the SVPWM is used to regulate the permanent magnet synchronous motor;Secondly,the gray wolf optimization algorithm is a new swarm intelligence optimization algorithm proposed in recent years,which has a simple optimization-seeking principle,multiple inputs and multiple outputs as well as a significant improvement in optimization speed and accuracy compared with the traditional swarm intelligence optimization algorithm.However,the standard gray wolf algorithm is prone to stagnation or local optimal solutions.To address the above drawbacks,the Improved Gray Wolf algorithm(IGWO)is proposed,and seven test functions are used to test it experimentally.Then,to verify the feasibility of the improved gray wolf algorithm for PID controller parameter rectification,therefore,this paper combines the improved gray wolf optimization algorithm with the traditional type of PID control.The PID parameter tuning based on the standard Gray Wolf and improved Gray Wolf algorithms are simulated and compared under four different classical arithmetic models,and the results show that the improved Gray Wolf optimization algorithm outperforms the standard Gray Wolf algorithm in PID parameter tuning.Finally,the simulation model of PMSM control system with optimized parameters of PID control optimized by IGWO is constructed in MATLAB/Simulink.In order to verify the superiority of the improved Gray Wolf algorithm for PID parameter tuning in permanent magnet synchronous motor,simulations are conducted in Genetic Algorithm(GA),Whale Algorithm(WOA)and Gray Wolf Algorithm(GWO)under three different conditions.The simulations show that the PMSM speed control system based on the traditional PID control optimized by the IGWO algorithm achieves the advantages of reduced overshoot,improved response speed and strong anti-disturbance capability.