A Synergistic Predictive Fusion Control Method and Application for Steering Feel Feedback of Steer-by-Wire System

Abstract

In the steer-by-wire system of an intelligent vehicle, the steering feel feedback system provides accurate and real-time steering feel for a driver by precisely controlling the output torque of the steering feel feedback motor. The precision of output torque depends on the control effect of motor current, which is mainly affected by the key factors, including the dynamic performance of the control system, sampling error, and motor parameter variation. To provide accurate and real-time steering feel, how to design a control method considering the above influence factors has been an acknowledged challenging issue. To solve this problem, a synergistic predictive fusion (SPF) control method is proposed in this article. First, to improve the dynamic performance of the control system, the combined synergistic current control algorithm with the deadbeat predictive (DP) control principle and the synergistic predictive (SP) current control is proposed. Under this framework, a current correction algorithm is designed for the sampling error, and a weighting factor is obtained from the current ratio to adjust the weight of the sampling current. Meanwhile, considering the influence of parameter variation on current control error, an improved Adaline network parameter estimation algorithm is constructed to dynamically adjust motor parameters, and an input signal feedback adjustment step factor is added to enhance the parameter tracking ability. Finally, the simulation and test prove that using the proposed method can track steering feel more quickly (up to 10.61%) and more accurately (up to 13.56%) than using the traditional DP control method.