上海交通大学学报

上海交通大学学报 >

2022 , Vol. 56 >Issue 5: 594 - 603

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.108

机械与动力工程

驾驶机器人转向操纵的动态模型预测控制方法

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  • 南京理工大学 机械工程学院, 南京 210094
姜俊豪(1997-) 男,浙江省温州市人,硕士生,主要从事驾驶机器人研究.

收稿日期: 2021-04-09

  网络出版日期: 2022-06-07

基金资助

国家自然科学基金(51675281);中央高校基本科研业务费专项资金(30918011101);江苏省研究生科研与实践创新计划资助项目(KYCX20_0362)

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Dynamic Model Predictive Control Method for Steering Control of Driving Robot

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  • School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received date: 2021-04-09

  Online published: 2022-06-07

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摘要

为实现驾驶机器人对试验车辆进行精确的转向操纵,提出了一种驾驶机器人转向操纵的动态模型预测控制方法.建立了驾驶机器人与被操纵车辆的耦合动力学模型,并对耦合模型的可控性进行判别.利用卡尔曼滤波器对耦合模型进行状态估计,并结合估计状态设计了模型预测控制器.将最小二乘法用于路径曲率与预测时域的非线性关系的拟合,设计了具有可变预测时域的动态模型预测控制器.进行了不同驾驶工况下的驾驶机器人转向操纵仿真与试验,研究结果表明了所提方法的有效性.

关键词： 驾驶机器人; 转向操纵; 卡尔曼滤波器; 预测时域; 动态模型预测控制

本文引用格式

姜俊豪, 陈刚 . 驾驶机器人转向操纵的动态模型预测控制方法[J]. 上海交通大学学报, 2022 , 56(5) : 594 -603 . DOI: 10.16183/j.cnki.jsjtu.2021.108

Abstract

A dynamic model predictive control method for driving robots is proposed to realize accurate steering control of the test vehicle. First, the coupling dynamics model of the driving robot and the controlled vehicle is established, and the controllability of the coupling model is judged. Then, the Kalman filter is used to estimate the state of the coupled model, and a model predictive controller is designed according to the estimated state. The least square method is adapted to fit the nonlinear relationship between path curvature and prediction horizon, and a dynamic model predictive controller with variable prediction horizon is designed. Finally, the simulation and the test of the steering control of the driving robot at different conditions are conducted, and the results verify the effectiveness of the proposed method.

Key words： driving robot; steering control; Kalman filter; prediction horizon; dynamic model predictive control

参考文献

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