使用深度神经网络的不确定非线性连续时间严格反系统的在线终身最佳跟踪控制
1Dept. of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, 65401, MO, USA.
概括
本研究介绍了一种使用整体强化学习 (IRL) 和深度神经网络 (DNN) 的最佳轨迹跟踪方法. 该方法提高了非线性系统的控制性能,在模拟中实现了显著的成本降低.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 严格反形式的非线性连续时间系统对轨迹跟踪提出了挑战.
- 传统的后退方法需要重复计算虚拟控制器衍生品,增加复杂性.
- 深度神经网络 (DNN) 提供了强大的函数近似能力,但可能会遭受消失梯度和灾难性遗忘.
研究的目的:
- 为非线性系统开发一种基于整体强化学习 (IRL) 的全新最佳轨迹跟踪方案.
- 通过结合动态表面控制来减轻与传统后退相关的计算负担.
- 用先进的技术来解决DNN培训的挑战,如消失的梯度和灾难性的遗忘.
主要方法:
- 利用整体强化学习 (IRL) 结合后退和深度神经网络 (DNN).
- 在最佳框架内采用动态表面控制,以简化衍生计算.
- 针对关键演员的DNN实施了在线单一值分解 (SVD),以最大限度地减少折扣值函数和减轻消失梯度.
- 引入了终身学习 (LL) 技术,以防止在DNN中发生灾难性遗忘.
主要成果:
- 为演员和评论家DNN开发了基于SVD的新型在线重量更新规则,有效地减轻消失梯度.
- 成功实施了在线终身学习 (LL) 技术,以克服灾难性遗忘.
- 通过分析和模拟证明了闭环稳定性.
- 与现有文献相比,实现了移动机器人跟踪和船舶自动驾驶模拟的总成本降低76%.
结论:
- 拟议的基于IRL的最佳轨迹跟踪方案有效地以严格的反形式处理非线性系统.
- 集成动态表面控制,SVD和终身学习显著提高了基于DNN的控制性能和稳定性.
- 该方法在现有方法上提供了显著的改进,实际应用中的显著成本降低证明了这一点.
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