Fuzzy Sliding Mode Control for Dynamic Walking Assistance in Lower Limb Exoskeletons

Abstract

Controlling lower limb exoskeletons presents substantial challenges due to the nonlinear dynamics of human gait and the need for adaptation to varying user requirements and environmental conditions. To address these challenges a fuzzy sliding mode Control (FSMC) strategy as an advanced hybrid control solution for a 2-DOF lower limb exoskeleton robot designed to assist dynamic walking has been proposed in this paper. The key novelty of the proposed method lies in the integration of fuzzy logic into the sliding mode control framework, where the fuzzy inference system dynamically adjusts the control gains based on real-time tracking errors and their rates of change. This adaptive mechanism enables FSMC to leverage the advantage of the high robustness of sliding mode control and the smooth response characteristics of fuzzy logic systems. The proposed FSMC was benchmarked against classical sliding mode control(SMC) and Quasi-SMC across multiple walking scenarios, including variations in walking speed and random stop-start motions. Unlike these SMC methods, which often struggle with nonlinearities, parameter uncertainties, and gait variability, the proposed FSMC approach offers superior improved tracking and robustness outperforming both the classical SMC and Quasi-SMC. Across all gait scenarios normal, increased, and random FSMC consistently outperforms both conventional SMC and Quasi-SMC in chattering suppression while maintaining superior tracking precision. Key performance metrics demonstrate FSMC’s dominance reduction in Chattering Index (CI), lower Total Variation (TV), and decrease in Chattering Energy Index (CEI), all while achieving better tracking accuracy with lower Integral Square error(ISE). The system’s ability to reduce chattering further supports its suitability for applications where energy efficiency and reduced mechanical wear are critical.