The Authors Declare that They Have No Known Competing Financial Interests or Personal Relationships that Could Have Appeared to Influence the Work Reported in this Paper

This paper proposes a cuckoo search optimisation coupled with improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a local optimal solution. First, the tent chaotic mapping method is used to initialize the population instead of using random initialization, so as to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population.Finally, inspired by the cuckoo search optimisation (CSO) algorithm, Levy flight strategy is introduced to update the position equation, so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions, and then proves that the proposed PID parameter tuning method is better than the traditional Z-N parameter tuning method and other group intelligent optimisation algorithms through the control simulation experiment of the servo motor.