A novel SOSMC-based COA-COOT approach for a multi-joint lower extremity exoskeleton system

Robotic exoskeletons offer significant potential for enhancing human capabilities and supporting medical rehabilitation. However, their control remains challenging due to nonlinear dynamics, parameter uncertainties, and external disturbances. To address these issues, this study proposes an AI-based adaptive control framework integrating a Second-Order Sliding Mode Controller (SOSMC) with a novel Coot Inherited Coati Optimization (COTI-CO) algorithm. The proposed hybrid optimizer combines the global exploration capability of the Coati Optimization Algorithm (COA) with the local exploitation capability of the Coot Optimization Algorithm (COOT) to optimally tune SOSMC gains. MATLAB simulations demonstrate improved tracking accuracy, faster convergence, reduced computational cost, and enhanced robustness under disturbances compared with GWO, COA, COO, and WO.