Extreme-learning-machine-based FNTSM control strategy for electronic throttle

A novel extreme-learning-machine-based robust control scheme for automotive electronic throttle systems with uncertain dynamics is presented in this paper. It is shown that the well-known extreme learning machine (ELM) is used to estimate the upper bound of the lumped uncertainty while a fast nonsingular terminal sliding mode feedback controller is designed to achieve global stability and finite-time convergence for the closed-loop system. Although the ELM used in this paper has the same structure as the one in the conventional least-square-based ELM used for pattern classifications, i.e., the input weights are randomly chosen, the ELM adopted in the closed-loop control system is designed to achieve global control purpose. The output weights of the ELM will be adaptively adjusted in Lyapunov sense from the perspective of global stability of the closed-loop system, rather than local optimization in conventional ELM. The proposed control can thus not only realize the finite-time error convergence but also needs no prior knowledge of lumped uncertainty. Simulation results are demonstrated to verify the excellent tracking performance of the proposed control in comparison with other existing control methods.