A Synergistic Predictive Fusion Control Method and Application for Steering Feel Feedback of Steer-by-Wire System
A Synergistic Predictive Fusion Control Method and Application for Steering Feel Feedback of Steer-by-Wire System
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DOI：
10.1109/tte.2022.3193762
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发表时间：
2023-03
影响因子：
7
通讯作者：
Chao Yang;Yipeng Gao;Weida Wang;Yuhang Zhang;Ying Li;Xiang-yu Wang;Xun Zhao
中科院分区：
文献类型：
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作者：
Chao Yang;Yipeng Gao;Weida Wang;Yuhang Zhang;Ying Li;Xiang-yu Wang;Xun Zhao
在智能汽车的线控转向系统中，转向手感反馈系统通过精确控制转向手感反馈电机的输出扭矩，为驾驶员提供准确、实时的转向手感。输出扭矩的精度取决于电机电流的控制效果，而这主要受控制系统的动态性能、采样误差以及电机参数变化等关键因素的影响。为了提供准确、实时的转向手感，如何设计一种考虑上述影响因素的控制方法一直是公认的具有挑战性的问题。为解决这一问题，本文提出了一种协同预测融合（SPF）控制方法。首先，为提高控制系统的动态性能，提出了一种结合无差拍预测（DP）控制原理和协同预测（SP）电流控制的协同电流组合控制算法。在此框架下，针对采样误差设计了一种电流校正算法，并根据电流比值得到一个加权因子，以调整采样电流的权重。同时，考虑到参数变化对电流控制误差的影响，构建了一种改进的自适应线性神经元（Adaline）网络参数估计算法来动态调整电机参数，并增加了一个输入信号反馈调整步长因子以增强参数跟踪能力。最后，仿真和试验证明，与传统的DP控制方法相比，采用本文提出的方法能够更快（最高提升10.61%）、更准确（最高提升13.56%）地跟踪转向手感。
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.