基于终身学习的最佳轨迹跟踪 控制使用深度神经网络限制非线性亲系系统的控制
IEEE transactions on cybernetics
|June 12, 2024
概括
本研究引入了一种新的终身整体强化学习 (LIRL) 方法,用于在复杂系统中实现最佳轨迹跟踪. 它通过加强控制策略和防止多任务场景中的内存损失,显著降低了机器人应用程序的成本.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 最佳的轨迹跟踪对于机器人系统至关重要.
- 有状态约束的不确定非线性系统带来了重要的控制挑战.
- 终身学习对于在动态环境中调整控制政策至关重要.
研究的目的:
- 开发一种基于终身综合强化学习 (LIRL) 的最佳轨迹跟踪方案.
- 为了应对不确定的非线性连续时间 (CT) 亲系系统与状态约束的挑战.
- 为了改善控制政策的产生和减轻多任务系统中的灾难性遗忘.
主要方法:
- 使用关键多层神经网络 (MNN) 或深层NN来近似值函数并生成最佳控制策略.
- 采用基于单一值分解 (SVD) 的方法在线调整关键MNN权重.
- 整合了一个在线终身学习 (LL) 计划,以防止灾难性遗忘.
- 使用时间变化的屏障函数 (TVBF) 解决了状态约束.
主要成果:
- 实现了对不确定的非线性CT亲系系统的最佳轨迹跟踪.
- 在多任务系统中有效缓解灾难性遗忘.
- 通过TVBF.成功地处理了国家约束.
- 通过利亚普诺夫稳定性分析展示了闭环系统的统一终极边界性 (UUB).
结论:
- 提出的基于LIRL的最佳框架有效地解决了受约束的非线性系统中的轨迹跟踪问题.
- 基于SVD的新调整和LL方案提高了控制政策的适应性和稳定性.
- 在双链机器人操纵器上的实验结果显示,总成本降低了47%,验证了该方法的实际有效性.
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