适应性有限时间基础的神经最佳控制时间延迟的轮式移动机器人系统
Shu Li1, Tao Ren1, Liang Ding2
1The Key Laboratory of Intelligent Control Theory and Application of Liaoning Provincial, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一种自适应的神经控制方法使用有限时间技术处理具有不确定的时间延迟的非线性系统. 这种方法确保了系统稳定性和追踪错误在有限的时间框架内趋同.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 具有时间延迟的非线性系统带来了重大的控制挑战.
- 适应控制和神经网络对于处理系统不确定性是有效的.
- 有限时间控制比传统的无限时间控制提供了更好的性能.
研究的目的:
- 为具有不确定的状态时间延迟的非线性系统设计一种自适应的神经最佳跟踪控制方法.
- 为了确保有限时间的融合和控制系统的优化.
- 为了保证半全球实用的有限时间稳定性和有限时间跟踪错误的趋同.
主要方法:
- 使用适当的Lyapunov-Krasovskii函数 (LKF) 来管理时间延迟.
- 定义一个具有有限时间集成的新型非二次式汉密尔顿-雅各比-贝尔曼 (HJB) 函数.
- 采用完整的强化学习来解决HJB函数和设计跟踪控制器.
- 在强化学习框架内实施神经网络权重的梯度下降更新.
主要成果:
- 拟议的方法有效地处理非线性系统中不确定状态的时间延迟.
- 设计的控制器确保了有限时间的融合和系统的优化.
- 保证了半全球实用的有限时间稳定性和有限时间跟踪错误的趋同.
结论:
- 适应性神经最佳跟踪控制方法为具有时间延迟的非线性系统提供了强大的解决方案.
- 有限时间方法提高了系统性能和稳定性.
- 这项工作有助于对复杂动态系统的智能控制策略的进步.
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