基于强化学习的适应性最佳控制,用于非线性系统与不对称的hysteresis
概括
本研究涉及使用演员-批判性学习的普兰特尔-伊什林斯基歇斯底里斯的非线性系统的适应性最佳跟踪. 一种新的方法可以弥补歇斯底里,使得尽管存在系统不确定性,但仍能有效控制.
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.
- 机器学习 机器学习
背景情况:
- 非线性亲系系统经常表现出复杂的行为,如歇斯底里,特别是在执行器.
- 普兰特尔-伊什林斯基 (PI) 歇斯底里在实现精确的系统跟踪和最佳控制方面带来了重大挑战.
- 歇斯底里模型中的不确定性使标准控制和优化技术的直接应用变得复杂.
研究的目的:
- 调查受不对称PI歇斯底里影响的非线性亲系系统的适应性最佳跟踪问题.
- 开发一个强大的控制方案,克服动力驱动器动力学中歇斯底里所带来的困难.
- 为了使演员-关键 (A-C) 学习机制的应用,以在歇斯底里存在时实现最佳控制.
主要方法:
- 开发一个反向模型,以使用转移因子来补偿PI歇斯底里.
- 设计一个反控制器,将估计和近似误差集成到汉密尔顿函数的误差项中.
- 在非线性补偿后通过哈密尔顿函数的部分导数推导最佳控制规律.
主要成果:
- 在执行器模型中成功补偿了不对称的普兰特尔-伊什林斯基歇斯底里非线性.
- 实现了汉密尔顿函数的构建,尽管歇斯底里引起的输入延迟.
- 通过模拟验证,实现了针对非线性系统的目标类的适应性最佳跟踪.
结论:
- 拟议的基于演员关键学习的方案有效地解决了PI歇斯底里系统中的适应性最佳跟踪问题.
- 反向模型补偿策略成功地减轻了hysteresis对控制系统性能的影响.
- 开发的方法为在具有执行器非线性性的复杂非线性系统中进行最佳控制设计提供了一种可行的方法.
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