PID parameter tuning optimization based on multi-strategy fusion improved zebra optimization algorithm

Abstract

The PID controller is one of the common control strategies in automatic control systems and is applied in various practical scenarios. Optimizing the design of PID controllers is an important topic at present. In this article, to solve the disadvantages of traditional PID parameter tuning methods such as time-consuming, prone to local search, complex calculation, and unclear termination criteria, a PID parameter tuning strategy based on multi-strategy fusion improved zebra optimization algorithm (MZOA) is proposed. For a series of problems such as the zebra optimization algorithm (ZOA) is prone to local optimization and slow convergence speed, the chaotic mapping and householder mirror reflection learning are combined to initialize the population, improve the distribution quality of the initial population in the search space, and introduce the tangent flight strategy based on the tangent search algorithm. The tangent flight strategy can stably produce a larger step length throughout the iteration, optimize the global search ability of the algorithm, and avoid falling into the local optimum. In the stage of resisting predator attacks, a sine–cosine optimization algorithm on hyperbolic cosine enhancement factor is introduced, using its oscillation to disturb the population and enhance the global search ability. Finally, the improved zebra optimization algorithm is used to optimize the parameters of the PID controller, and the MZOA-PID parameter tuning model and the ZOA-PID parameter tuning model are simulated. The simulation results show that compared with ZOA, MZOA has higher convergence accuracy and performance, can tune PID parameters faster, and makes the actual output curve of PID control parameters closest to the theoretical output curve.