Motor Speed Control of Four-wheel Differential Drive Robots Using a New Hybrid Moth-flame Particle Swarm Optimization (MFPSO) Algorithm

Abstract

Speed control of DC motors is essential for automated vehicles and four-wheel differential drive (4WD) cars, which are distinct by their high level of maneuverability. The PID controller is one of the most popular techniques for controlling speed, but tuning its parameters is challenging. This paper presents a novel hybrid algorithm, the Moth-Flame Particle Swarm Optimization (MFPSO), which combines moth-flame optimization (MFO) and particle swarm optimization (PSO) to address the slow convergence of MFO and the premature convergence of PSO. The MFPSO is deployed for real-time interactive tuning of the PID controller to control the speed of DC motors in a 4WD car. Additionally, a novel practical procedure is proposed to build a robust four-wheel differential drive and maintain the synchronization of the four DC motors. Simulation results and statistical analysis demonstrate the superior performance of the MFPSO compared with the PSO, MFO, and other hybrid variants (HMFPSO and HyMFPSO), with MFPSO ranking first in the Friedman test on CEC2020/2021 and engineering optimization benchmark problems. Practical results and the transient response analysis of the speed control revealed that MFPSO significantly outperformed the traditional Ziegler-Nichols (ZN) method, MFO, PSO, HMFPSO, and HyMFPSO algorithms. Specifically, the MFPSO algorithm reduced settling time by 34.83%, 21.20%, 20.75%, 22.97%, and 31.59%, and overshoot by 86.11%, 64.99%, 71.02%, 74.37%, and 60.58% compared to the ZN, MFO, PSO, HMFPSO, and HyMFPSO algorithms, respectively. The source code of the proposed algorithm is available at https://github.com/MohamedRedaMu/MFPSO-Algorithm.