Sliding Mode Position Tracking Control-Based Waterwheel Plant Algorithm for Elbow Rehabilitation Systems

Joint dysfunction affects millions of people worldwide, severely limiting their ability to carry out fundamental daily activities such as eating, drinking, and writing. These limitations often hinder independent self-care. To address this challenge, rehabilitation robots have been developed to assist individuals with physical impairments in performing daily tasks and preserving their autonomy. In this study, a sliding mode control (SMC) for position tracking of unit step and sinusoidal signal inputs of the elbow rehabilitation (Elb-Rehab) systems is proposed. In the context of performance enhancement of the SMC, two reaching laws are examined. The first reaching law is based on the hyperbolic tangent function (SMC_tah), whereas the second one is based on the saturation function (SMC_sat). SMC's designed parameters are tuned using the waterwheel plant algorithm (WPA) based on the integral of absolute error (IAE) as a cost function. Simulation experiments based on MATLAB software are used to examine the two control algorithms for the Elb-Rehab system, which are then analyzed. The simulation outcomes reveal that the tracking error based on the IAE of the system controlled by the SMC_sat is reduced by 12.68% and 76.3% compared to that of the system controlled by the SMC_tah for the unit step and sinusoidal signal inputs respectively. Furthermore, under an uncertainty scenario, the SMC_sat continues to outperform the SMC_tah. However, the SMCtah shows slightly less sensitively to uncertainty than the SMC_sat.