PSO Tuned Super-Twisting Sliding Mode Controller for Trajectory Tracking Control of an Articulated Robot - Zewdalem Abebaw Ayinalem; Abrham Tadesse Kassie (Journal of Electrical and Computer Engineering)
These days, robots excel at speed, precision, and reliability, surpassing human capabilities. Articulated manipulators, though, pose challenges due to their complex, nonlinear nature and susceptibility to uncertainties such as parameter changes, joint friction, and external disturbances. Designing robust trajectory tracking control for these dynamics is a key focus. This paper introduces a novel method that integrates SolidWorks modeling to create precise digital representations of the robot’s mechanical structure, facilitating easier development and simulation of control algorithms. To drive the robot joints, a permanent magnet direct current motor is used. Initially, sliding mode control (SMC) was employed, but it resulted in chattering in the control’s input response. To mitigate this issue and enhance trajectory tracking, this paper designs a super-twisting SMC (STSMC). Intelligent particle swarm optimization (PSO) is employed to obtain optimal parameter values for STSMC, ensuring consistency, stability, and robustness. A comparative analysis was conducted among PSO–STSMC, STSMC, PSO–SMC, and classical SMC. Numerical simulations revealed that the tracking error and root mean square error (RMSE) improvements were approximately 18.33%, 16.66%, and 14.29% for PSO–STSMC compared to STSMC, and 79.50%, 78.04%, and 25.0% compared to PSO–SMC for each of the three joints under ideal conditions, respectively. Numerical simulations demonstrate the proposed controller’s robustness to external disturbances, parameter variations, and joint friction, effectively mitigating chattering effects in the control signal. Notably, this article highlights the potential of the PSO–STSMC for practical implementation in articulated robotic systems and underscores its significance in advancing robust trajectory tracking control, providing insights into articulated robot control strategies.