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.