Zhao Chunbo, Yan Huaran, Gao Deyi
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
PeerJ Comput Sci. 2024 Dec 10;10:e2605. doi: 10.7717/peerj-cs.2605. eCollection 2024.
In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is introduced to convert the compound uncertain element, comprising uncertain dynamics and time-varying ocean disturbances, into a linear parametric form with just one unknown parameter. Then, to improve the tracking performance of the UUVs trajectory tracking closed-loop control system, an actor-critic neural network structure based on ADHDP technology is introduced to adaptively adjust the weights of the action-critic network, optimizing the performance index function. Finally, an ADHDP-based robust self-learning control scheme is constructed, which makes the UUVs closed-loop system have good robustness and control performance. The theoretical analysis demonstrates that all signals in the UUVs trajectory tracking closed-loop control system are bounded. The simulation results for the UUVs validate the effectiveness of the proposed control scheme.
在这项工作中，我们提出了一种基于动作依赖启发式动态规划（ADHDP）的鲁棒自学习控制方案，以解决具有不确定动力学和时变海洋干扰的欠驱动无人水下航行器（UUV）的三维轨迹跟踪控制问题。首先，引入径向基函数神经网络，将由不确定动力学和时变海洋干扰组成的复合不确定元素转换为仅含一个未知参数的线性参数形式。然后，为提高UUV轨迹跟踪闭环控制系统的跟踪性能，引入基于ADHDP技术的actor-critic神经网络结构，自适应调整动作-评论家网络的权重，优化性能指标函数。最后，构建了基于ADHDP的鲁棒自学习控制方案，使UUV闭环系统具有良好的鲁棒性和控制性能。理论分析表明，UUV轨迹跟踪闭环控制系统中的所有信号都是有界的。UUV的仿真结果验证了所提出控制方案的有效性。
