Optimal FOPID Speed Control of DC Motor via Opposition-Based Hybrid Manta Ray Foraging Optimization and Simulated Annealing Algorithm

Abstract

In this study, a fractional-order proportional–integral–derivative (FOPID) controller was used for controlling the speed of direct current (DC) motor. The parameters of the controller have optimally been adjusted using a new meta-heuristic algorithm, namely the opposition-based (OBL) hybrid manta ray foraging optimization (MRFO) with simulated annealing (SA) algorithm (OBL-MRFO-SA). The proposed novel OBL-MRFO-SA algorithm aims to improve the original MRFO algorithm in two ways. Firstly, it provides MRFO a better exploration capability with the aid of opposition-based learning. In this way, it can avoid local minimum stagnation. Secondly, it enables MRFO to have a better exploitation capability with the aid of hybridization using simulated annealing algorithm. The hybridization helps accelerating the convergence rate of MRFO. A time domain objective function which takes the performance criteria (maximum overshoot, steady-state error, rise time and settling time) into account has been used to design the FOPID based speed control system for DC motor with OBL-MRFO-SA algorithm. The performance of the proposed novel algorithm has been assessed through various analyses such as time and frequency domain simulations, robustness and load disturbance rejection. Compared to other state-of-the-art optimization algorithms, OBL-MRFO-SA has shown superior exploration and exploitation capabilities. The performance of the developed algorithm has also been demonstrated to be better by using a physical setup.