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

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.

Automated summary

The study utilized the OBL-MRFO-SA algorithm to optimize the parameters of the FOPID controller for DC motor speed control, improving performance through time and frequency domain simulations, robustness analysis, and load disturbance rejection. The algorithm demonstrated superior exploration and exploitation capabilities compared to other optimization algorithms.