0278-0046 (c) 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIE.2018.2795591, IEEE Transactions on Industrial Electronics 17-TIE-1335 1 Abstract—The control of automobile electronic throttle (AET) systems is a challenging task owing to the multiple nonlinearities, i.e., transmission friction, gear backlash, limp-home spring set, and external load disturbance. In this paper, a practical tracking control scheme of an AET system is developed using continuous fast nonsingular terminal sliding mode (CFNTSM) technique based on uncertainty observer. By using the prescribed CFNTSM surface and fast terminal sliding mode-based reaching element, the proposed control implementation guarantees the fast error convergence characteristic and high tracking accuracy under parameter uncertainties and perturbations. Furthermore, due to the adoption of the finite-time exact observer (FEO) for the lumped uncertainty estimation in the controller, the ease of the selection of the control gains is well achieved since they only depend on the uncertainty estimation error. The closed-loop stability and finite-time convergence are presented based on the Lyapunov stability theory. Experimental verifications are conducted to validate the remarkable performance of the proposed control, in terms of the step and sinusoidal tracking as well as anti-disturbance ability. Index Terms—Automobile electronic throttle (AET), continuous nonsingular terminal sliding mode (CNTSM), finite- time exact observer (FEO), robustness. I. INTRODUCTION ECENTLY, automotive electronic throttle (AET) systems are widely used in automobile engine control systems, as the replacement of the mechanical ones where the valve plate is directly connected to the accelerator pedal with a wire [1]. In traditional engine throttle systems, the internal fuel efficiency and external road and weather conditions are not considered in obtaining the valve plate angle and thus the overall engine efficiency is greatly affected. The AET systems can simultaneously regulate the engine charge air and fuel resulting in the precise air-to-fuel ratio control, particularly for the transient engine working conditions. The advantages of the AET systems are that not only are the fuel consumption and gas emission reduced, but also the automobile drivability and the passengers’ comfort are improved. A typical AET system involves a DC motor, a reduction gear set, a throttle body, and a limp-home (LH) spring set maintaining the valve plate at its default angle. For the practical AET system connected to the engine, there exist multiple parameter uncertainties including the parameters of the motor and the throttle valve, i.e., the moment of inertia and the damping coefficients. More importantly, the nonsmooth characteristic nonlinearities which include the rotational static and dynamic frictions, LH spring set, and gear backlash greatly affect the tracking accuracy of the AET system. Furthermore, the torque load introduced by the intake air flow leads to the additional external disturbance to throttle system. Therefore, all the above parameter uncertainties and nonlinearities have to be suppressed in order to suit for high-accuracy tracking purposes of AET systems. The successful treatment for the parameter uncertainties and the high nonlinearities depends on the suitable design of the control methodologies. Although linear proportional integral derivative (PID) control with feed-forward compensation (FC) and optimal control [2]-[5] were studied, the consistent output tracking performance and the robustness may not be assured particularly when a large range of parameter uncertainties and perturbations appear. Further, neural network (NN) control and fuzzy control have been developed to deal with the complex uncertainties and nonlinearities [6]-[7], but the adopted techniques cannot cover the whole range of operating conditions for AET systems. Alternatively, in [8]-[10], adaptive control has also been utilized to estimate the system information; however, the closed-loop performance cannot be well maintained if the system model varies significantly due to the plant uncertainty. Due to the remarkable advantages of the sliding mode (SM) control in maintaining the robust performance and improving the disturbance rejection ability [11]-[13], the SM control has been successfully applied to AET control systems [14]-[17]. However, the SM control has two main drawbacks: on one hand, a large fixed switching control gain was used to suppress the effects of the large uncertainties and disturbances, leading to the severe control chattering and large control amplitude. Although the undesired chattering can be removed by using the so-called boundary layer (BL) method [17], it was at the cost of degradating the tracking performance and robustness. Also, time-varying switching gain-based SM control [18] has been proposed to achieve chattering reductions and good performance. On the other hand, as the linear sliding surface is adopted in the SM-based AET control systems, the finite-time convergence of the closed-loop system can be Continuous Fast Nonsingular Terminal Sliding Mode Control of Automotive Electronic Throttle Systems Using Finite-time Exact Observer Hai Wang, Member, IEEE, Liheng Shi, Zhihong Man, Member, IEEE, Jinchuan Zheng, Member, IEEE, Shihua Li, Senior Member, IEEE, Ming Yu, Member, IEEE, Canghua Jiang, Huifang Kong, and Zhenwei Cao R Manuscript received May 03, 2017; revised August 05, September 20, and December 06, 2017; accepted for publication January 02, 2018. This work was supported in part by the National Nature Science Funds of China under Grant 61503113, Grant 61771178, and Grant 61673154. (Corresponding author: Hai Wang.) H. Wang, L. Shi, M. Yu, C. Jiang, and H. Kong are with the School of Electrical and Automation Engineering, HFUT, Hefei 230009, China (email: wanghai0652@163.com; 836574747@qq.com; mltrym@hotma il.com; chjiang@hfut.edu.cn; konghuifang@163.com). Z. Man, J. Zheng, and Z. Cao are with the Faculty of Sciences, Engineering, and Technology, Swinburne University of Technology, Melbourne, 3122, Australia (email: zman@swin.edu.au; jzheng@sw in.edu.au; zcao@swin.edu.au). S. Li is with the School of Automation, Southeast University, Nanjing 210096, China (email: lsh@seu.edu.cn).