Event-Triggered Based Trajectory Tracking Control of Under-Actuated Unmanned Surface Vehicle with Input Quantization and Output Constraints

The under-actuated unmanned surface vehicle (USV) trajectory tracking control problem is examined in this paper in relation to output constraints, model uncertainties, and external disturbances. To alleviate the pressure of the limited communication bandwidth of USV at sea, this paper uses a composite quantizer to linearly describe the quantization process. For the problem of under-actuated USV with two available inputs, controllers are designed based on the backstepping algorithm and the theory of the Barrier Lyapunov function (BLF), respectively, so as to address the problem of output constraints. Then, to realize the compensation of the uncertainty, an adaptive neural network system is used for the approximation. In addition, while ensuring effective tracking of the under-actuated USV, to save communication resources more effectively and reduce the frequency of controller execution, this paper adopts the event-triggered mechanism in the controller design. It is demonstrated through stability analysis that the output constraints will not be broken, guaranteeing that the system's outputs will remain within a manageable range and that all signals will be eventually bounded while preventing Zeno behavior. Finally, simulation results are used to validate the efficacy of the control mechanism suggested in this paper.