Proportional-Integral-Derivative Parametric Autotuning by Novel Stable Particle Swarm Optimization (NSPSO)

To improve the performance, robustness and stability of autotuning the proportional integral and derivative (PID) parameter, the novel stable particle swarm optimization (NSPSO) is proposed in this paper. The NSPSO is the combination of the particle swarm and optimization algorithm with the new stable rule to reconsider the survival of the remaining particle in the search space for handling the instability of the system. The new rule is proposed based on proving the stability according to the Lypunov stability theorem. Additionally, to show the method’s superiority in performance and robustness, the proposed method is compared with the results of simulations with the particle swarm optimization (PSO), the hybrid particle swarm optimization-grey wolf optimization (PSO-GWO), the whale optimization algorithm (WOA) and the social spider optimization algorithm (SSO) based on a direct current (DC) motor control system. In the comparative performance, the various fitness functions are applied, while the comparative robustness and the changed operation point of the DC motor are applied. After comparing the methods, the proposed method obtains better results than the PSO, PSO-GWO, WOA and SSO in both performance and robustness.