Deep Neural Network-Based Adaptive Control for Unmanned Surface Vehicles With Uncertain Dynamics

This article focuses on the application issue of deep neural networks for the control strategy in unmanned surface vehicles. A novel adaptive law is developed to update the weights of the deep neural networks, thereby enhancing the control strategy’s ability to handle uncertain dynamics. We establish the relationship between the output of the deep neural networks and the dynamic response of the unmanned surface vehicle using the command filter method. And then, we develop a convex optimization-based adaptive approach to address the challenges of online learning within the deep neural network. We also prove the convergence of the signals in our controller using the Lyapunov stability theorem. Finally, simulation and experiment results are given to demonstrate the effectiveness of our proposed method.