干扰拒绝事件触发的强大模型预测控制,用于追踪受约束的不确定机器人操纵器
IEEE transactions on cybernetics
|September 6, 2023
概括
一种新的控制方法结合了机器人操纵器的计算扭矩类控制 (CTLC) 和基于干扰观察者的事件触发的强大模型预测控制 (DO-ET-RMPC). 这种方法增强了轨迹跟踪,同时有效地管理约束和干扰.
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
- 应用数学 应用数学 应用数学
背景情况:
- 机器人操纵器需要精确的轨迹跟踪,尽管外部干扰和系统限制.
- 现有的控制方法往往难以平衡稳定性,计算效率和约束满意度.
- 模型预测控制 (MPC) 提供了一个有希望的框架,但可能是计算密集的.
研究的目的:
- 为机器人操纵者提出一个新的等级控制框架.
- 为了提高在边界干扰和状态/控制输入约束下轨迹跟踪性能.
- 提高模型预测控制 (MPC) 的稳定性和计算效率.
主要方法:
- 一个分层的控制框架,结合了计算机扭矩控制 (CTLC) 和基于干扰观察器的事件触发的强大模型预测控制 (DO-ET-RMPC).
- CTLC用于线性化和解机器人的非线性动态,简化后续的控制设计.
- 在DO-ET-RMPC中使用双模式MPC方法,以确保强大的稳定性和约束满足,同时允许事件触发操作.
主要成果:
- 拟议的CTLC-DO-ET-RMPC框架有效地实现了机器人操纵器的轨迹跟踪.
- 该方法成功处理受界干扰,同时满足状态和控制输入约束.
- 包括Zeno回避,强大的约束满足,递归可行性和稳定性在内的理论性质已被证明是连续时间系统的.
- 模拟证明了拟议的控制方案在现有方法上的优越性.
结论:
- 开发的层次控制框架为机器人操纵器轨迹跟踪提供了强大且计算效率高的解决方案.
- CTLC和DO-ET-RMPC的组合在管理复杂的动态,干扰和约束方面提供了显著的优势.
- 这项工作在机器人系统的强有力的控制策略方面取得了重大进展.
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