基于RL的适应性模糊优化跟踪控制用于受约束的交换式随机非线性系统:修改后的AED-ADT方法
IEEE transactions on cybernetics
|August 7, 2025
概括
本研究介绍了非线性系统的强化学习 (RL) 适应性模糊控制策略. 它优化了跟踪性能,并处理了无时间限制的异步切换,避免了Zeno行为.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 非线性系统动态 非线性系统动态
背景情况:
- 缓慢切换的非线性系统具有随机干扰,这给控制带来了重大挑战.
- 现有的事件触发控制方法经常与异步切换和性能优化作斗争.
- 规定的设置时间性能约束需要先进的控制策略.
研究的目的:
- 开发基于强化学习 (RL) 的适应性模糊事件触发的优化跟踪控制策略.
- 在缓慢切换的非线性系统中解决异步切换问题.
- 在规定的设置时间约束下优化系统跟踪性能.
主要方法:
- 一个优化的事件触发机制,用于子系统处理无时间限制的异步切换.
- 一个强化学习 (RL) 策略,以显著提高跟踪性能.
- 修改了允许的边缘依赖ADT方法和稳定性分析的最佳控制器.
- 参数3和性能约束一致性的正常化函数.
主要成果:
- 拟议的事件触发机制有效地解决了异步切换问题.
- 强化学习显著优化了系统跟踪性能.
- 已证明闭环系统信号的局限性,并且避免了Zeno现象.
- 该策略的优越性通过模拟和单环操纵器示例来验证.
结论:
- 开发的基于RL的自适应模糊事件触发控制策略为缓慢切换的非线性系统提供了卓越的性能.
- 该方法有效地管理异步切换和随机干扰,同时确保稳定性并避免Zeno行为.
- 这项工作为非线性系统中复杂的控制任务提供了强大而优化的解决方案.
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