| 引用本文: | 姚文龙,庞震,池荣虎,邵巍.环卫车辆轨迹跟踪系统的无模型自适应迭代学习控制[J].控制理论与应用,2022,39(1):101~108.[点击复制] |
| Yao Wen-long,Pang Zhen,Chi Rong-hu,Shao Wei.Track tracking control of sanitation vehicle based on model-free adaptive iterative learning control[J].Control Theory and Technology,2022,39(1):101~108.[点击复制] |
|
| 环卫车辆轨迹跟踪系统的无模型自适应迭代学习控制 |
| Track tracking control of sanitation vehicle based on model-free adaptive iterative learning control |
| 摘要点击 1720 全文点击 547 投稿时间：2020-11-12 修订日期：2021-11-30 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2021.00797 |
| 2022,39(1):101-108 |
| 中文关键词 环卫车辆 移动机器人 轨迹跟踪 无模型自适应控制 迭代学习控制 扰动 |
| 英文关键词 sanitation vehicles mobile robots trajectory tracking model-free adaptive control iterative learning control disturbances |
| 基金项目 青岛市自主创新重大专项(21–1–2–14–zhz), 山东省重大科技创新工程(2021SFGC0601), 国家自然科学基金项目(61873139), 山东省自然科学基金 项目(ZR2017MEE071)资助. |
|
| 中文摘要 |
| 针对环卫车辆周期重复性工作特点, 考虑模型时变以及未知扰动问题, 提出一种基于无模型自适应迭代学
习的环卫车辆轨迹跟踪控制方法. 首先, 针对环卫车辆建立了两轮移动机器人的运动学模型, 然后, 给出带时变参数
和非线性不确定项的迭代域下全格式动态线性化数据模型, 引入时间差分估计算法, 设计基于最优性能指标的轨迹
跟踪无模型自适应迭代学习控制方法, 并进行仿真分析. 结果表明, 环卫车轨迹跟踪系统车身角随迭代增加超调减
小, 与传统迭代学习控制算法相比, 具有松弛的条件限制和较好的鲁棒性, 同时提高了控制系统精度. |
| 英文摘要 |
| In this paper, a trajectory tracking control method for sanitation vehicles based on model-free adaptive iterative
learning is proposed considering the problems of model time-varying and unknown disturbance aiming the periodic
repetitive work characteristics. First of all, the kinematics model of two-wheeled mobile robot is built for the sanitation
vehicle. Secondly, a full-format dynamic linearized data model with time-varying parameters and nonlinear uncertainties
in the iterative domain is presented. Then a model-free adaptive iterative learning control (MFAILC) algorithm with time
difference estimation algorithm for trajectory tracking based on the optimal performance index is designed. At the end, the
simulation analysis was carried out. The research results show that the body angle of the trajectory tracking system of the
sanitation vehicle decreases with the increase of iteration. Compared with the traditional iterative learning control algorithm,
the improved method has slack conditional constraints and better robustness, and the control accuracy is improved. |
|
|
|
|
|