基于 (N + 1) 阶段梯度学习的非线性离散时间系统的高级最佳跟踪控制.
概括
本研究介绍了使用改进的N步梯度学习算法对非线性系统的先进最佳控制方法. 它增强了趋同,并消除了跟踪错误,而不需要折扣因子或二次式输入项.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 非线性动力学是一种非线性动力学.
背景情况:
- 非线性离散时间系统在实现快速融合和零追踪误差方面存在挑战.
- 现有的N步梯度学习算法通常依赖于无限的未来奖励假设和二次式输入项.
研究的目的:
- 为非线性离散时间系统开发先进的最佳控制方法.
- 为了加快融合性能和消除跟踪错误.
- 引入一个新的跟踪错误指数,独立于折扣因子.
主要方法:
- 提出了一个改进的 (N+1) 步骤梯度学习算法.
- 引入了一种新的跟踪错误指数,避免二进制输入术语.
- 价值代 (VI) 和政策代 (PI) 方法用于分析趋同和稳定性.
- 为了实现,采用了一个具有四个神经网络的演员-关键结构.
主要成果:
- 拟议的算法实现了更快的融合,并消除了跟踪错误.
- 该方法在不计算参考控制输入的情况下获得最佳控制策略.
- 收性,单调性,最佳性和稳定性属性是没有零初始函数假设的.
- 在直升机系统上的模拟证明了该方法的有效性.
结论:
- 先进的最佳控制方法有效地解决了非线性最佳跟踪挑战.
- 这种新的方法提高了非线性离散时间系统的性能.
- 演员关键实现验证了拟议的算法的实际可用性.
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