This paper solves the path following problem of unmanned surface vessels, where model uncertainties, external environmental disturbances, and input saturation are considered. Specifically, the fixed-time line of sight (FXTLOS) based on fixed-time theory is used to design a guidance subsystem where the convergence time is independent of multiple initial states. Given the unavailability of velocity information, novel cascaded extended state observers have been proposed to estimate velocities and unknown disturbances to provide feedback to the controller for better performance, where only position and heading information is required. Furthermore, fast finite-time sliding mode control is used to solve the motion control problem for the USV, where a suitable auxiliary system is designed to deal with the adverse effects of input saturation and to ensure the stability of the system. The stability of the whole system is demonstrated, and the stability of the kinematic and dynamic cascade system is analyzed. Finally, a large number of comparative simulation cases are given to demonstrate the effectiveness and superior performance of the proposed scheme.

Path following and obstacle avoidances are used in heading control and path replanning for unmanned surface vessels (USVs), which have attracted the interest of many researchers over the years. This paper investigates the path-following and obstacle avoidance problems for USVs. First, on the basis of the current position and desired path, an adaptive line-of-sight (ALOS) algorithm is used to obtain the desired heading angle, and the sideslip angle compensation is considered. Then, to ensure that the USV follows the desired path. Model predictive control (MPC) is used to reduce the lateral error. The event-triggered mechanism (ETM) strategy is utilized to reduce the computational cost of MPC. Moreover, to obtain accurate state quantities in real time, a linear extended state observer (LESO) is used to counteract the effects of external disturbances and the nonlinear term of the model. Furthermore, an improved obstacle avoidance algorithm based on the geometric relationship is proposed. This method can better ensure USV navigation safety and reduce consumption and computation. Lastly, multiple simulation experiments illustrate that the algorithm improves the path-following capability and security and ensures smooth input changes by setting input constraints. Therefore, the designed controller has better feasibility and robustness.