Self-Tuning Backstepping Control with Kalman-like Filter for High-Precision Control of Automotive Electronic Throttle

Abstract

The automotive electronic throttle (AET) control system has been widely applied in modern automotive engines, and accurate control of AET can improve engine performance as well as reduce pollution emissions. However, the noise in the sensor circuit and the variation in automotive driving conditions seriously affect the control performance of the AET system, making controller designing challenging. This paper proposes a self-tuning backstepping control with a Kalman-like filter (SBCKLF) strategy. First, the noise affecting the position sensor measurement is verified to be non-Gaussian by acquiring and processing the noise signal. To eliminate its influence on control precision, a Kalman-like filter is introduced to estimate the real position of the valve. Then, a self-tuning backstepping controller is designed to automatically adapt to the variation in vehicle driving conditions. A self-tuning algorithm based on fuzzy control is used to tune the parameters of the backstepping controller online, so as to optimize the controller performance. Finally, an experimental platform based on dSPACE for the AET control system is built to perform the controller comprehensive test in a real-time environment. Experimental results and performance analysis demonstrate the effectiveness of the proposed SBCKLF strategy. Compared to the best results of other methods, the proposed method reduces the maximum and root mean square tracking errors by 21.65% and the average error by 12.89%. The steady-state and tracking error bounds are controlled to 0.9° and 2.3°, respectively. It also shows that the SBCKLF strategy has the strongest robustness as well as the best response speed.