Optimized Super Twisting Sliding Mode Controller for Robot Manipulators

This paper investigates the use of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) al-gorithms to optimize the Super-twisting Sliding Mode Control (STSMC) technique for robot manipulators. STSMC is a power-ful control technique known by its accuracy but it requires a care-ful tuning of its parameters. By employing PSO and GWO, this study aims to enhance the control performance by automating the tuning process. Simulation results show that both algorithms significantly improve STSMC's robustness, stability, and tracking accuracy besides the enhanced convergence performance. This study highlights the potential of using meta-heuristic optimization techniques in advancing robotic control systems.