Abstract
Motivated by achieving precise positioning of the throttle plate, an extended state observer–based intelligent double integral sliding mode control strategy is proposed for the electronic throttle control system. Specifically, an extended state observer is first designed to observe the opening angle change of the electronic throttle and compensate unexpected external disturbance. On this basis, an intelligent double integral sliding mode controller for electronic throttle valve is presented based on Lyapunov stability theory and sliding mode control theory, which includes stability analysis and parameter adaptation design. Finally, extensive simulations are conducted to demonstrate the superior performance of the proposed method.
Highlights
In past decades, a variety of electronic throttle (ET) control methods, which have great significance on drivability of automobiles and help reduce fuel emissions, have been proposed
The BP neural network is used for online self-adaption of the control parameters of double integral sliding mode controller (DISMC)
The tracking errors of intelligent double integral sliding mode controller (IDISMC) is smaller than sliding mode control (SMC) after parameters variations, which means the accuracy of the proposed controller is higher than SMC, and the robustness of IDISMC is much better than SMC
Summary
A variety of electronic throttle (ET) control methods, which have great significance on drivability of automobiles and help reduce fuel emissions, have been proposed. The main factor restricting the performance of ET control is that the system model includes a barrage of nonlinear characteristics (i.e. stick-slip friction, gear backlash, and nonlinear spring) and external disturbances, but not all of previous studies have already taken these factors into account Inspired by this observation, this article proposes an ESO-based intelligent double integral sliding mode controller with improved transient performance and strong robustness. The tracking errors of IDISMC is smaller than SMC after parameters variations, which means the accuracy of the proposed controller is higher than SMC, and the robustness of IDISMC is much better than SMC
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