Lyapunov-based control for enhanced trajectory tracking in robotic arms using multi-objective particle swarm optimization

Robot manipulators have become a revolutionary tool in modern automation, medical interventions, service robots, rehabilitation systems, and service industries, acting as enabling methodologies to enhance the precision and reliability of many applications such as robotic-assisted telehealth. It is therefore evident that, to maximize functionality, large-scale and powerful control strategies need to be harnessed by such robotic systems. This work presents a new control scheme for two degrees of freedom robot arm based on Lyapunov PID control and Multi-Objective Particle Swarm Optimization (MOPSO). The proposed approach aims to consider reduction of tracking errors and control effort. These two objective optimizations simultaneously increase the precision of the trajectories and decrease the wear of the mechanism and energy consumption, which are invaluable properties for practical problems such as robot cutting and welding. The proposed approach demonstrates significantly better simulation performance with advanced reference trajectories, resulting in notable improvements in tracking accuracy and energy efficiency compared to well-established methods. By combining Lyapunov-based analysis with bioinspired multi-objective optimization, this work contributes to a more robust and reliable controller design framework for nonlinear robotic systems. The proposed framework thus provides a systematic solution for advanced robotic design by overcoming the limitations of heuristic or trial-and-error methods.