Dual-line PID controller based on PSO for speed control of DC motors

This paper proposes an AI-based heuristic optimization techniques based on particle swarm optimization (PSO) to solve two applications i.e. identification of a DC motor and tuning PID controller parameters. The identification of a DC motor application, the 2^nd order transfer function with time delays is obtained by the searching poles location mechanism based on PSO. This method has following steps. First, time responses of speed are determined by multi-level step input voltages. Second, the noises of time responses are reduced by wavelet soft thresholding. Finally, the noiseless time responses are identified by PSO. The tuning PID controller parameters application, the gains of proportional, integral, and derivative are determined both off-line and on-line tuning. For off-line tuning, the PID gains are optimized by PSO. On-line application, PSO-gains scheduling, adaptively adjusts the controller gains to improve the speed response under changing the speed demand and load. The simulation results show that the PSO can be efficiently used for system identification and tuning parameters of PID controller.