Multivariable Fuzzy Sliding-Mode Torque Control for Internal Combustion Engines

Internal combustion engine torque control presents a persistent challenge due to pronounced nonlinearities, parametric uncertainties, and time-varying dynamics. While conventional controllers like the proportional–integral derivative (PID) are widely implemented, they often struggle to deliver high-performance results under transient conditions. To address this gap, this work introduces and experimentally validates a novel torque controller with fuzzy sliding-mode controller (FSMC) architecture, a hybrid control not previously applied to the domain of engine torque regulation. The proposed FSMC is specifically engineered to systematically mitigate the effects of system nonlinearities by integrating the robustness of sliding-mode theory with the adaptive, chattering-suppression capabilities of fuzzy logic. This study details the controller’s development, implementation, and rigorous experimental validation on an ethanol-fueled engine via a dynamometer test bench. The controller’s performance was benchmarked against a standard PID controller, demonstrating the FSMC’s capacity for high-fidelity reference tracking, achieving mean rise and fall times up to 1.4 s and a mean absolute error not exceeding 0.2 Nm. These results signify a substantial advance in control performance and engine safety, filling the identified gap in the literature and underscoring the potential of the proposed fuzzy sliding-mode strategy as an effective and robust solution for advanced torque control in internal combustion engines.