This paper presents a novel Adaptive Sliding Mode Control (ASMC) strategy for nonlinear multivariable systems subjected to parameter uncertainties and external disturbances. The proposed control scheme guarantees robust and smooth state convergence via an adaptive mechanism that dynamically adjusts the switching gain. Unlike conventional SMC techniques, this adaptive formulation effectively mitigates the chattering phenomenon through a continuously updated boundary layer and eliminates the need for prior knowledge of the uncertainty bounds. The effectiveness of the synthesized controller is validated on an autonomous electric vehicle (AEV) platform, a system characterized by strong dynamic coupling. MATLAB/Simulink (version 2022b) simulations are conducted under various operational scenarios, including load variations and strict trajectory tracking. Comparative results with a traditional SMC demonstrate superior convergence, significant chattering reduction, and an optimized energy consumption profile, leading to a 22% reduction in equivalent CO2 emissions. This approach provides a viable and energy-efficient control framework for modern autonomous EVs.
Lajmi et al. (2026) studied this question.