计算机科学
人工神经网络
自适应控制
人工智能
控制工程
学习迁移
数据建模
控制系统
适应(眼睛)
适应性学习
自适应系统
控制(管理)
机器学习
控制理论(社会学)
最优控制
实验数据
系统动力学
主动学习(机器学习)
曲面(拓扑)
反向传播
动态规划
传递函数
智能控制
强化学习
作者
Yuxiang Zhang,Yifan Cheng,Shuzhi Sam Ge,Bernard Voon Ee How
标识
DOI:10.1109/tii.2025.3619904
摘要
The abundant knowledge of data and physics models can be simultaneously utilized in learning-based modeling, prediction, and control methods, which makes the balance between model efficiency, accuracy, and complexity. Thus, this work investigates the physics-informed neural networks (PINNs)-based adaptive optimized control method with essential learning designs for the whole learning framework. More specifically, the proposed method efficiently realizes the adaptive learning performance with PINNs modeled system dynamics via continuous learning with online data and system physics. Meanwhile, the PINNs model with autodifferentiation is employed by the adaptive dynamic programming approach to iteratively approximate the solution of the continuous-time Hamilton–Jacobi–Bellman equation with neural networks, providing more accuracy and efficiency over approaches that solely utilize either data-driven or physics-based models. As an outcome, the proposed method enables the PINNs-based learning control method to have superior performance in model transfer adaptation and learning efficiency. The proposed method is applied to automated vessel control problems, and its effectiveness and practical applicability are demonstrated through comparative simulations and hardware-in-the-loop tests.
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