对不确定的多输入多输出系统的自适应量化有限时间容错控制及其应用
Yue Sun1, Ming Chen1, Yu-Lin Gai1
1School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan Liaoning, 114051, China.
ISA transactions
|November 12, 2024
概括
这项研究引入了一种非线性系统的新控制方法,其中包括执行器故障和输入量化,提高响应速度和稳定性. 适应性模糊逻辑控制确保可靠的性能,即使部分执行器故障.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 模糊逻辑应用 模糊逻辑应用
背景情况:
- 多输入多输出 (MIMO) 非线性系统面临的挑战是执行器故障和输入量化.
- 现有的控制方法往往需要对控制收益的预先了解,或与故障适应作斗争.
研究的目的:
- 为具有执行器故障和输入量化的MIMO非线性系统开发一种新的状态反控制策略.
- 提高系统响应速度和对不确定性和故障的稳定性.
- 为了避免在适应性控制方案中假设未知的控制收益.
主要方法:
- 使用模糊逻辑系统 (FLS) 来近似不确定的虚拟控制规律.
- 实施有限时间控制以增强系统响应.
- 集成一个适应性控制方案,用于部分损失故障增益与输入量化.
- 采用利亚普诺夫稳定性分析进行理论验证.
主要成果:
- 拟议的控制方案确保在有限的时间内跟踪错误的趋同和受限的闭环信号,即使有系统故障.
- 适应性策略成功地估计了未知的故障增益,没有事先的假设.
- 对于四旋翼无人驾驶飞行器 (UAV) 态度系统的模拟结果证明了拟议方法的有效性.
结论:
- 新的状态反控制方法有效地解决了MIMO非线性系统中的执行器故障和输入量化.
- 模糊逻辑和有限时间控制的整合提供了一个强大而高效的解决方案.
- 该方法为无人机等复杂动态系统的耐故障控制提供了显著的进步.
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