This paper investigates the problem of asymptotic stabilization of underactuated surface vehicles (USVs) with actuator dead-zones and yaw constraints. By transforming the system model into two subsystems, a backstepping control method based on a Barrier Lyapunov Function (BLF) is introduced to stabilize the transformed system and prevent the USV from reaching the boundaries of yaw angle and velocity constraints. A fixed-time disturbance observer is involved in the USV controller to deal with actuator dead-zones and disturbances. The method is extended to cover the case in which the yaw constraints are time-varying and the initial states exceed the constraint regions. Numerical simulations have shown the effectiveness of the presented method.

This study explores positioning and obstacle avoidance control for autonomous surface vehicles (ASVs) by considering equivalent elliptical-shaped obstacles. Firstly, compared to most Barrier Lyapunov function (BLF) methods that approximate obstacles as circles, a novel BLF is improved by introducing an elliptical obstacle model. This improvement uses ellipses instead of traditional circles to equivalent obstacles, effectively resolving the issue of excessive conservatism caused by over-expanded areas during the obstacle equivalence process. Secondly, unlike traditional obstacle avoidance approaches based on BLF, to achieve constraint control of angle and angular velocity, a method based on model predictive control (MPC) is introduced to optimize local angle planning. By incorporating angular error constraints, this ensures that the directional error of the ASV remains within a restricted range. Furthermore, an auxiliary function of directional error is introduced into the ASV’s linear velocity, ensuring that the ASV parks and adjusts its direction when the deviation in angle becomes too large. This innovation guarantees the linearization of the ASV system, addressing the complexity of traditional MPC methods when dealing with nonlinear second-order ASV systems. Ultimately, the efficacy of our proposed approach is validated through rigorous experimental simulations conducted on the MATLAB platform.

In this paper, the finite-time bipartite consensus (FTBC) problem is investigated for the multi-agent system (MAS) with detail balanced structure. To realize FTBC of MAS, a unified protocol framework is developed. Some criteria are established for realizing FTBC. It is worth noticing that estimations of settling time can be given in form of mathematical expression. The unified framework can bring in various protocols by choosing different parameters, which extends previous results. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of corresponding theoretical results.

This paper investigates the trajectory tracking problem of autonomous ground vehicles (AGVs). The dynamics considered feature external disturbances, model uncertainties, and actuator dead zones. First, a novel time-varying yaw guidance law is proposed based on the line of sight method. By a state transformation, the AGV is proved to realize trajectory tracking control under the premise of eliminating guidance deviation. Second, a fixed time dead zone compensation control method is introduced to ensure the yaw angle tracking of the presented guidance. Furthermore, an improved fixed-time disturbance observer is proposed to compensate for the influence of the actuator dead zone on disturbance observation. Finally, the trajectory tracking control strategy is designed, and simulation comparison shows the effectiveness of the compensate method. The CarSim–MATLAB cosimulation shows that the proposed control strategy effectively makes the AGV follow the reference trajectory.