Design And Application For Fractional-order PID Control Algorithm

PID(Proportional Integral Derivative,PID)is a classic controller that calculates the control law based on the error of the system to improve the system's rapidity,accuracy and stability.PID controller is widely used in linear time invariant systems because it does not need to know the exact model of the system in the process of use and has the advantages of simple structure and easy adjustment of parameters.However,because of the linear nature of the PID controller,it does not perform well in complex nonlinear time-varying systems and complex industrial control processes where the object is uncertain.Therefore,the improvement of the traditional PID control algorithm has important theoretical value and practical engineering significance.In this paper,the principle and implementation of the traditional PID control algorithm are deeply analyzed,and the defects and shortcomings of the traditional PID controller are analyzed.The mathematical basis of fractional derivative is discussed,and the idea is introduced into the I and D dimensions of integer PID controller.Then combined with intelligent optimization algorithm,a fractional-order PID parameter tuning method based on improved teaching-learning based optimization algorithm is proposed.In the optimization algorithm,the teaching factors,mutual learning strategies and adaptive wavelet mutation strategies are improved to optimize the second-order system.In this paper,numerical simulation and physical test are completed.The numerical simulation is carried out by optimization algorithm and control system simulation.The simulation results proved that the proposed optimization algorithm can quickly and globally converge,and the designed fractional-oder PID control algorithm has good control performace.The hardware design and PID controller software of the self-balacing car are completed.Through the system debugging,the balance control and speed tracking control of the self-balance were realized,and experiments verify the feasibility of the control system.