Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning
By Rongxin Cui, Chenguang Yang, Y. Li, Sanjay K. Sharma. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2017
468 citations recorded by the source corpus.
In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV’s control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/ab688b92a0fc790c4f3641d0f94a7d61cdf54231
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Citations in the corpus
- Leader–Follower Formation Control of USVs With Prescribed Performance and Collision Avoidance (2019, background): “the closed-loop system is a challenging issue, especially when the vehicle dynamics are subject to modeling uncertainties [12]– [17] and unknown disturbances from maritime environments”