Optimized Control for Underactuated Surface Vessels Trajectory Tracking: Combining Radial Basis Neural Network with Minimum Learning Parameters and Adaptive Nonlinear Feedback Technique to Address FDIAs

This research examines how false data injection attacks (FDIAs) impact the trajectory tracking control of underactuated surface vessels (USVs). The internal uncertain dynamics of the system are reconstructed using radial basis function neural networks (RBFNNs). In order to avoid the computational pressure of the RBFNNs on the system, the neural network weights, external disturbances, and FDIAs are converted into a single parameter learning form using the minimum learning parameters (MLPs). Next, a nonlinear feedback function is constructed and introduced into the controller design process, thereby avoiding the controller accuracy loss caused by MLPs. Within the backstepping method framework, the adaptive laws leverage deep information robust adaptive technology to estimate the upper limits of the uncertainty term. The closed-loop system is provided with a rigorous theoretical analysis by combining the Lyapunov stability theory. Finally, the effectiveness of the control scheme is verified by simulation. The results show that the proposed controller guarantees boundedness of all closed-loop signals and drives the tracking errors into a small neighborhood of the reference trajectory even under the attack of FDIAs and the influence of internal and external uncertainties.