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

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