Abstract
This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for vehicular electronic throttle (VET) systems with parametric uncertainties, lumped uncertainty and actuator constraint. The proposed control strategy adopts a fixed-time sliding mode (FTSM) dynamical structure which ensures a fixed time convergence property for both reaching motion and sliding motion. For relaxing the upper bound information constraint of sliding mode control design, an ELM-based mechanism is utilized for learning the lumped uncertainty bound online, while the ELM output weights are updated adaptively in the sense of Lyapunov. Also, considering that the input voltage is normally limited due to the vehicular battery constraint for practical applications, to handle the input saturation scenario, an auxiliary system (AS) is further designed to guarantee a fixed time convergence of auxiliary state and the state is correspondingly fed into the controller design. The global stability analysis of the closed-loop system is rigidly given. Comparative experimental studies are conducted to illustrate the excellent performance of the proposed control.