对于具有干扰的严格反非线性系统,基于观察者的干扰安全控制的结构设计
Zhong-Qiu Chen1, Jie Tao1, Ming Lin1
1School of Automation, Guangdong-Hong Kong Joint Laboratory of Intelligent Decision and Cooperative Control, Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China.
本研究引入了一种新的安全控制方法,用于面临未知干扰的非线性系统. 它结合了干扰观察,过后退和控制屏障功能 (CBFs) 来实现强大而高效的机器人控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 非线性系统分析 非线性系统分析
背景情况:
- 现有的安全后退控制方法需要精确的模型,限制对现实世界的干扰的稳定性.
- 严格反的非线性系统容易受到未知的外部干扰,这给安全运行带来了挑战.
研究的目的:
- 为在未知的干扰下安全控制严格反非线性系统开发一种新的结构性控制框架.
- 提高现有安全控制技术的稳定性和减轻其局限性.
主要方法:
- 整合一个干扰观察员来估计和补偿未知的干扰.
- 在控制器设计中,将过后退与控制屏障功能 (CBF) 结合使用.
- 递归设计框架用于构建流的虚拟和实际控制器.
主要成果:
- 拟议的框架有效地估计和补偿未知的干扰,提供比传统的强大方法更为保守的方法.
- 统一的方法成功地减轻了"复杂性爆炸"并避免了不平滑的问题.
- 通过数值模拟和实时实验对机器人手臂进行验证.
结论:
- 新的框架为未知干扰的非线性系统的安全控制提供了有效的解决方案.
- 与现有方法相比,该方法提高了稳定性和计算效率.
- 在机器人操纵器上证明了实际应用.
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