A Novel Adaptive Control Scheme for Automotive Electronic Throttle Based on Extremum Seeking

To achieve rapid and high-precision servo control of an electronic throttle, an adaptive control scheme is proposed based on the extremum seeking (ES), which consists of a variable-gain adaptive proportional-integral (ES-API) controller and an adaptive compensator (ES-ACP). The two gains ( ${K_{p}}$ , ${K_{i}}$ ) of the ES-API controller are designed as maps with respect to the tracking error, and the parameters of these maps are learned by ES. Additionally, the ES-ACP is applied to compensate for the strong nonlinearity inherent in an electronic throttle control (ETC) system, whose parameters are also learned by ES. During parameter learning, an objective function is utilized to quantify the tracking error of the opening angle of the electronic throttle plate, and then the parameters are learned using a step reference signal and a ramp reference signal. ES optimizes the above parameters by reducing the objective function to achieve a more favorable tracking response. Five reference signals are used to evaluate the learned controller after the parameter learning process is completed. Experiments were performed on a test bench equipped with an electronic throttle, and the experimental results show that the control scheme is capable of tracking multiple reference trajectories quickly and accurately.