部分不确定的非线性离散时间系统的最佳自适应跟踪控制,使用终身混合学习
IEEE transactions on neural networks and learning systems
|August 23, 2023
概括
本研究介绍了使用多层神经网络 (MNN) 的非线性系统的自适应追踪方法. 这种方法显著减少了机器人系统的追踪错误和累积成本,使终身学习成为可能.
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
- 机器人技术 机器人技术 机器人技术
背景情况:
- 具有部分不确定性的非线性离散时间 (DT) 系统带来了重大控制挑战.
- 适应性跟踪控制需要强大的方法来处理系统动态和不确定性.
- 现有的方法可能会在终身学习场景中扎与收率和灾难性遗忘.
研究的目的:
- 为部分不确定的非线性DT系统开发一个最佳的自适应跟踪控制策略.
- 提高基于神经网络的控制的融合率和稳定性.
- 为了实现终身学习能力,防止控制系统的灾难性遗忘.
主要方法:
- 使用多层神经网络 (MNN) 具有演员关键架构,以近似价值函数和最佳控制策略.
- 实施了批评NN权重更新的混合学习方案,结合了即时和代调整.
- 包含一个重复缓冲器,用于并发学习,以解决持续激发 (PE) 条件.
- 使用控制输入和时间差误差 (TDE) 来调整演员和批评者MNN权重,减轻消失梯度.
- 整合了一个权重整合方案,用于终身学习和预防灾难性遗忘.
主要成果:
- 使用Lyapunov分析证明了边界追踪错误和重量估计错误.
- 在双链路机器人操纵器上实现了显著的44%的跟踪错误减少.
- 在多任务环境中,累计成本减少了31%.
- 验证了混合学习和权重整合计划的有效性.
结论:
- 拟议的基于MNN的最佳自适应跟踪控制对部分不确定的非线性DT系统有效.
- 新的混合学习和并发学习策略增强了融合和稳定性.
- 终身学习能力得到了成功的整合,防止了灾难性的遗忘,并降低了累积成本.
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