ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs - Chunbo Zhao; Huaran Yan; Deyi Gao (PeerJ Computer Science)
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.