适应性固定时间跟踪控制大规模非线性系统基于改进简化优化后退策略的改进
Yushan Cen1, Liang Cao1, Hongru Ren2
1College of Mathematical Sciences, Bohai University, Jinzhou, 121013, Liaoning China.
ISA transactions
|January 14, 2025
概括
本研究为大型非线性系统提出了一种新的最佳固定时间控制策略. 该方法通过神经网络和强化学习来确保快速,有限的跟踪性能.
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.
- 人工智能的人工智能
背景情况:
- 大规模的非线性系统由于其复杂性和相互连接性而存在重大控制挑战.
- 实现最佳的跟踪性能与保证的融合时间对于许多应用程序至关重要.
- 现有的方法经常在复杂系统中与合速度和参数调整作斗争.
研究的目的:
- 为非严格反的大型非线性系统开发一个最佳的固定时间跟踪控制策略.
- 为了确保追踪错误在固定的时间内在规定的性能限制内趋同.
- 通过使用神经网络来提高融合率和简化控制算法.
主要方法:
- 使用固定时间控制技术与简化强化学习算法相结合.
- 开发新的批评者和演员神经网络,更新法律以实现最佳控制.
- 实施最小参数方法以减少适应性法律的复杂性.
- 将规定的性能控制纳入绑定跟踪错误.
主要成果:
- 拟议的控制策略保证追踪错误在固定的时间内在规定的范围内趋同.
- 该方法加快了融合率,并简化了最佳控制算法.
- 所有闭环信号都被证明在固定的时间间隔内受到限制.
- 模拟结果验证了拟议的控制策略的有效性.
结论:
- 开发的最佳固定时间控制策略有效地解决了大型非线性系统的跟踪控制.
- 神经网络的整合,强化学习和规定的表现提供了一个强大的解决方案.
- 该方法为复杂的动态系统提供了更快的融合和保证的性能限制.
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