Motion control system of slewing bearing based on Improved Adaptive Mutation Particle Swarm Optimization

Abstract

The response performance and speed follow-up performance of the slewing bearing control system affects the whole equipment performance directly. The core of the system is PID controller, however, whether the optimal parameters could be found or not seriously affects the performance of the controller. In this paper, the mutation operation of genetic algorithm (GA) was introduced and the particle mutation part was taken to particle swarm optimization (PSO) to overcome the deficiency of falling into partial optimum solution and eliminate the influence on system performance resulted from the improper selection of initial controller parameters, which was called adaptive mutation particle swarm optimization (AMPSO). And based on the AMPSO, a simplified particle mutation rule was presented and the mutation object was expanded to the entire population of the particles compared to AMPSO, which is called improved adaptive mutation particle swarm optimization (IAMPSO). The simulation results show that the K_p, K_i parameters obtained by this algorithm is superior to that of the original PSO algorithm; and the experimental results verified the effectiveness of this proposed control strategy.