TUNING OF TWO-DEGREE-OF-FREEDOM IMC BASED ON A STRANGE NONCHAOTIC OPTIMIZATION APPROACH FOR LARGE TIME-DELAY PROCESSES

Abstract

The internal model control (IMC) has a good control effect for large time-delay systems, but the parameter tuning of the controller is a challenge for researchers. As a typical fractal (strange) attractor, a strange nonchaotic attractor has the dynamic characteristics of randomness and ergodicity. Based on these characteristics, an optimization method is proposed to tune the parameters of an internal model controller for a large time-delay process, which is named a strange nonchaotic optimization algorithm (SNOA). SNOA has the advantages of easy implementation, short execution time and having robust mechanisms to avoid local optimization. It can find the optimal parameters of the internal model controller in large time-delay systems. The results show that SNOA can effectively improve the dynamic stability of the large time-delay systems. It has also proved to be superior to chaotic optimization (CO) algorithm and particle swarm optimization (PSO) algorithm.