| 引用本文: | 刘跃跃,王浩羽,吴小雨,樊启高.一类不确定非线性系统安全学习控制[J].控制理论与应用,2025,42(7):1323~1332.[点击复制] |
| LIU Yue-yue,WANG Hao-yu,WU Xiao-yu,FAN Qi-gao.Safe learning control for a class of uncertain nonlinear systems[J].Control Theory & Applications,2025,42(7):1323~1332.[点击复制] |
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| 一类不确定非线性系统安全学习控制 |
| Safe learning control for a class of uncertain nonlinear systems |
| 摘要点击 3366 全文点击 284 投稿时间：2023-05-26 修订日期：2024-12-06 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2024.30363 |
| 2025,42(7):1323-1332 |
| 中文关键词 高斯过程 反馈线性化 控制障碍函数 安全控制 二次规划优化 |
| 英文关键词 Gaussian process regression feedback linearization control barrier function safety learning control quadratic program |
| 基金项目 国家自然科学基金项目(62203186, 62373168), 江苏省“六大人才高峰”高层次人才项目(GDZB–138)资助. |
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| 中文摘要 |
| 针对非线性系统非参数不确定条件下的安全控制问题, 本文提出一种基于高斯过程(GP)的安全学习控制
方案. 首先, 基于在线采集到的历史数据, 利用高斯过程回归对非线性系统中的非参不确定性与时变扰动进行学习,
基于Lyapunov理论设计反馈线性化控制策略, 保证控制器全局一致最终有界(GUUB). 其次, 考虑到安全约束问题,
在反馈控制器的基础上, 利用控制障碍函数(CBF), 最小限度调整控制输入获得基于二次规划(QP)的优化控制输入.
此外, 分别在高概率意义上证明了闭环系统的有界性和状态安全域的前向不变性. 通过仿真结果, 验证了所提控制
策略在非参数不确定性下轨迹跟踪与避障约束中的有效性. |
| 英文摘要 |
| This paper addresses the safety control problem for nonlinear systems under nonparametric uncertainty conditions by proposing a control scheme based on Gaussian processes (GPs). Initially, leveraging historical data collected
online, GP regression is employed to learn nonparametric uncertainty and time-varying disturbances within the nonlinear
system. Subsequently, a feedback linearization control strategy is designed based on the Lyapunov theory, ensuring the controller’s global uniform ultimate boundedness (GUUB). Secondly, considering the safety constraints, based on the feedback
controller, control barrier function (CBF) is employed to minimize the control input, which obtains an optimal control input
through quadratic programming (QP). Moreover, the boundedness of the closed-loop system and the forward invariance of
the state safety domain are proved in a high-probability sense, respectively. Through simulation results, the effectiveness
of the proposed control strategy in trajectory tracking and obstacle avoidance constraints under non-parametric uncertainty
is verified. |
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