反推
强化学习
控制理论(社会学)
欠驱动
计算机科学
稳健性(进化)
最优控制
人工神经网络
李雅普诺夫函数
自适应控制
Lyapunov稳定性
控制工程
数学优化
机器人
控制(管理)
非线性系统
数学
人工智能
工程类
生物化学
化学
物理
量子力学
基因
作者
Lin Chen,Shi‐Lu Dai,Chao Dong
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-14
被引量:15
标识
DOI:10.1109/tnnls.2022.3214681
摘要
In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.
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