Research On Automobile Electronic Throttle Control System Based On Sliding Mode Variable Structure Control And Disturabnce Observer

With the development of intelligent automobile industry,modern electronic throttle(ET)system has been widely used in traditional internal combustion engine automobile and drive-by-wire automobile.ET system plays an important role in improving fuel efficiency,driving comfort and safety.In this dissertation,based on the theory of nonlinear disturbance observation and the theory of terminal sliding-mode control,the precise and robust control of the automotive electronic throttle system is studied.The operating principle and structure composition of the ET control systems are first elaborated in the dissertation.Taking the complex nonlinear dynamics and uncertainties into consideration,the uncertainty model is then established.Secondly,aiming at the control task of the system,two disturabance observer-based control strategies are proposed,namely,extreme learning machine(ELM)-based robust sliding mode control strategy and extended state observer(ESO)-based robust adaptive dynamic sliding mode control strategy.For extreme-learning-machine-based robust sliding mode control strategy,the wellknown 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 control system.The features of this control scheme are as follows: 1)Alghough the ELM used in the scheme has the same structure as the one in the conventaional least-square-based ELM,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 conventaionl ELM.2)The proposed control can not not only realize the finite-time error convergence but also needs no prior knowledge of lumped uncedrtainty due to the utilization of ELM estimator.For extended-state-observer-based robust adaptive dynamic sliding mode control strategy,a modified ESO is adopted to estimate the lumped uncertainty online and an adaptive dynamic sliding mode(ADSM)feedback control is then designed to achieve a robust closed-loop tracking performance.The features of this control scheme lie on that: 1)The modified ESO is employed to estimate system states and lumped uncertainty,where the estimation error is compensated with an adaptively updated switch gain,which mitages the cosntrain on the information of upper bound for the disturbance.2)The stability proof combining the observer with the feedback control is presented in detail.Finally,based on the practical driving behavior,the comparison simualtions and experiments are carried out considering multiple scenarios.Conpared with traditional linear control,traditional terminal sliding mode control and adaptive sliding mode control,the proposed two novel control schemes behave excellent trakcing performance with better convergence,robustness and stability.