Abstract
A robust adaptive chattering-free sliding mode (ACFSM) control method for electronic throttle (ET) system is proposed in this paper. It is well known that nonlinearities in the throttle system including friction, return-spring, limp-home (LH) and gear backlash affect the control accuracy of the throttle valve. Compared with the traditional sliding mode control methods, the ACFSM control not only overcomes the influence of nonlinearities and parameter uncertainties in the ET system, but also eliminates chattering in nature such that excellent throttle tracking performance and robustness are maintained. The proposed ACFSM control method is superior to the traditional sliding mode control in the following two aspects: 1) The chattering-free sliding mode control method is able to attenuate the control chattering without weakening the output tracking performance and robustness. 2) The upper bounds of the uncertainty and disturbance are not required any more in control design. They are online estimated by the adaptive law in the sense of Lyapunov. Moreover, due to the difficulty in selecting proper control parameters, the genetic algorithm (GA) is introduced to optimate the control parameters for the ACFSM controller prior to practical implementation. The comparative experimental results are given to demonstrate the excellent control performance of the proposed ACFSM control.
Highlights
As one of the important actuators in the gasoline engine management system, the vehicle throttle body directly influences the power, economy, safety, comfort and stability of the automobile
We find that the optimal values of a1, a2, r1 and r2 in Adaptive chattering-free sliding mode (ACFSM) controller are a1 = 54, a2 = 14, r1 = 0.28, r2 = 0.44, respectively
A digital implementation of the control algorithm is performed in a digital signal processor (DSP) development board (TMS320F28335) with the sampling interval of 1ms
Summary
As one of the important actuators in the gasoline engine management system, the vehicle throttle body directly influences the power, economy, safety, comfort and stability of the automobile. Since the friction compensator in [5] can cause high frequency oscillation in steady-state conditions, a self-learning PID controller using neural network technique was proposed by Yuan and Wang [6] to solve the problem. In order to solve the nonlinear problem of gearbox friction and limp, a constrained finite-time optimal control method was presented by Vašak et al [8]. Compared with traditional PID control, SMC method is more suitable for dynamic control process due to its ability of handling the effect of the sliding frictions and air flow pressure torque of the ET system, which means it can ensure a good tracking performance and strong robustness. Based on SMC methodology, an adaptive SMC method was proposed to online estimate the control gains and the uncertainty bounds such that the good performance was obtained [20]–[22]. The conclusion and some future work are given in the last section
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