The self-tuning PID control in a slider–crank mechanism system by applying particle swarm optimization approach

By using the particle swarm optimization (PSO) algorithm, a novel design method for the self-tuning PID control in a slider–crank mechanism system is presented in this paper. This paper demonstrates, in detail, how to employ the PSO so as to search efficiently for the optimal PID controller parameters within a mechanism system. The proposed approach has superior features, including: easy implementation; stable convergence characteristics; and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solutions. By using the PSO approach, both the initial PID parameters under normal operating conditions and the optimal parameters of PID control under fully-loaded conditions can be determined. The proposed self-tuning PID controller will automatically tune its parameters within these ranges. Moreover, the PC-based controller is implemented to control the position of the motor-mechanism coupling system. In order to prove the performance of the proposed PSO self-tuning PID controller, the responses are compared with those by the real-coded genetic algorithm (RGA) PID controller and the fixed PID controller. The numerical simulations and experimental results will show the potential of the proposed controller. 2006 Elsevier Ltd. All rights reserved.