将跟踪控制映射到离散严格反系统中的级联优化:一个层次化的学习方法
概括
本研究介绍了离散时间系统的层次学习框架,使得自适应跟踪控制成为可能. 该方法使用虚拟目标和代学习来优化系统性能并减少跟踪错误.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 优化理论 优化理论
背景情况:
- 具有严格反结构的离散时间 (DT) 系统对传统的跟踪控制构成了挑战.
- 现有的方法,如倒退,可能无法完全解决这些系统中的自适应控制的复杂性.
研究的目的:
- 为具有严格反的离散时间系统引入一种新的层次学习 (HL) 框架.
- 通过动态调整虚拟目标 (VTs) 并促进层间自我优化,实现有效的跟踪控制.
主要方法:
- 一个层次化的学习框架,对状态变量具有动态调节的虚拟目标 (VTs).
- 在每个层进行自优化的离散时间汉密尔顿-雅各比-贝尔曼 (HJB) 方程的近似值.
- 一个代的预测性学习框架,以解决非因果关系,并将VTs与最佳轨迹对齐.
- 行动网络转变为一个包含未来追踪错误的追踪网络.
主要成果:
- 拟议的HL框架允许间接跟踪状态变量以达到所需的目标.
- 该方法通过考虑未来的跟踪错误,优化跟踪网络重量,降低成本和提高性能.
- 收分析和数值模拟证明了该框架的有效性.
结论:
- 层次学习框架为离散时间系统中的自适应跟踪控制提供了一个强大的方法.
- 动态的VT调整和代学习有助于提高控制性能和系统稳定性.
- 这种方法显示出适应性控制工程应用的巨大潜力.
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