上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (10): 1210-1218.doi: 10.16183/j.cnki.jsjtu.2020.182

所属专题: 《上海交通大学学报》2021年“交通运输工程”专题 《上海交通大学学报》2021年12期专题汇总专辑

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考虑延迟的汽车线控转向系统自适应内模控制

刘文通, 陈俐(), 陈峻   

  1. 海洋工程国家重点实验室,上海 200240
  • 收稿日期:2020-06-15 出版日期:2021-10-28 发布日期:2021-11-01
  • 通讯作者: 陈俐 E-mail:li.h.chen@sjtu.edu.cn
  • 作者简介:刘文通(1997-),男,河南省驻马店市人,硕士生,研究方向为车辆动力学与控制.
  • 基金资助:
    国家自然科学基金资助项目(51875340)

Adaptive Internal Model Control for Automotive Steer-by-Wire System with Time Delay

LIU Wentong, CHEN Li(), CHEN Jun   

  1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2020-06-15 Online:2021-10-28 Published:2021-11-01
  • Contact: CHEN Li E-mail:li.h.chen@sjtu.edu.cn

摘要:

针对线控转向系统中的未知与不确定延迟,基于内模控制思想设计自适应控制策略,提高转角跟踪精度.为避免引入非最小相位项,提出采用全极点方法近似延迟环节,将延迟模型与线控转向系统模型相结合,构建新的名义模型,引入Kalman滤波方法辨识名义模型参数,实时更新内模控制的逆控制器增益.仿真和台架试验结果表明,与逆控制器未计入延迟的传统内模控制和经典比例、积分、微分(PID)控制相比较,考虑延迟的自适应内模控制跟踪误差较小,对延迟变化的适应能力较强.

关键词: 线控转向系统, 延迟, 自适应, 内模控制

Abstract:

Aimed at the unknown and uncertain time delay in the steer-by-wire system, an adaptive control strategy is designed based on the internal model control framework to improve the tracking accuracy of the steering angle. Without inducing non-minimum terms, a novel nominal model is established by connecting the model of the steer-by-wire system and the linearized delay model using the all pole approximation method. The Kalman filter algorithm is designed to identify the nominal model parameters and to update in real time. Thereafter, the parameters in the inverse controller of internal model controller are tuned online. The simulation and bench test results show that compared with the traditional internal model control whose inverse control discards time delay and the classical proportion integration differentiation (PID) control, the proposed adaptive internal model control considering time delay can obtain a better tracking accuracy and has a stronger adaptability to the change of time delay.

Key words: steer-by-wire system, time delay, adaptive, internal model control

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