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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Abdennour Zeghida
University of Badji Mokhtar
Control & Systems Engineering Electrical & Electronics Engineering

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Hybrid control strategy for trajectory tracking and obstacle avoidance in differential wheeled robots: integrating PSO-NMPC, GA, and fuzzy logic Abdennour Zeghida; Lotfi Farah; Halim Merabti; Abdelfateh Kerrouche
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1008-1024

Abstract

Mobile robots frequently encounter challenges in maintaining accurate trajectory tracking and effective obstacle avoidance in dynamic and uncertain environments. Traditional control methods, such as proportional integral derivative (PID) and standard MPC, often fail to provide the necessary adaptability and robustness for complex navigation tasks. To overcome these limitations, this study proposes a hybrid control framework for differential-drive wheeled robots that integrates particle swarm optimization–based nonlinear model predictive control (PSO-NMPC), adaptive neuro-fuzzy inference system (ANFIS) optimized by PSO, and genetic algorithm (GA) tuning. The PSO-NMPC computes optimal control inputs in real time while satisfying system constraints to ensure precise trajectory tracking, achieving an average RMSE of 0.0941 m (RMSEx = 0.0884 m, RMSEy = 0.0812 m). The ANFIS-PSO controller manages nonlinearities and environmental uncertainties for reliable obstacle avoidance, with an overall RMSE of 0.1084 m (RMSEx = 0.0761 m, RMSEy = 0.0772 m). The GA further optimizes key parameters and trajectories, ensuring global path refinement and robust obstacle clearance, achieving an overall RMSE of 0.1094 m (RMSEx = 0.1059 m, RMSEy = 0.0274 m). Simulation results in Matlab2024b confirm that the proposed hybrid framework provides precise trajectory tracking, smooth control, and robust obstacle avoidance, making it a promising solution for autonomous mobile robots operating in dynamic and uncertain environments.
Co-Authors Abdelfateh Kerrouche Halim Merabti Lotfi Farah