Optimizing PID-Based Controller Utilizing Hybrid Evolutionary Algorithmn in Electric Motor-Driven Exoskeletons for Therapeutic Locomotion of Stroke Patients

Wearable robots have become increasingly significant in rehabilitation treatments aimed at improving patients suffering from walking gait abnormalities. The effectiveness of these robots depends on their ability to accurately track trajectories. This paper proposes a hybrid technique for tuning a PID controller in a wearable lower limb rehabilitation robot (WLLR). The combination of GA and PSO, termed HGAPSO, is utilized to acquire PID parameters for the hip and knee joints, with the aim of minimizing overshoot and tracking error. Notably, the percentage overshoot recorded by HGAPSO for the hip and knee is superior to that of conventional ZN, GA, and PSO methods, with percentages of 4.9% and 0.42%, respectively. Furthermore, the maximum error (ME) and average error (AE) between desired and actual trajectories recorded for a range of motion (ROM) and walking conditions do not exceed 0.05, which are deemed acceptable errors. The maximum root mean square error (RMSE) recorded for both ROM and walking conditions is 0.028 and 0.043, respectively. Additionally, the coefficient of determination (R
²
) for both conditions is more than 99%, indicating a close fit between desired and actual trajectories under various conditions.