Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

This paper discusses the course tracking control of unmanned surface vehicles with actuator input quantization and an event-triggered mechanism. The system control laws are designed based on the backstepping method, combining dynamic surface control technology to mitigate the computational complexity expansion of virtual control laws. A fuzzy logic system can be used to approximate the uncertainties in the control system. The control system’s control inputs are quantized by using uniform quantizers. Then, the event-triggered adaptive fuzzy quantization control method is introduced, which can reduce the frequency of control actions and effectively reduce the communication burden. The stability of the control system is rigorously proven using Lyapunov stability theory, ensuring that all signals in the closed-loop system remain bounded. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness.