This paper is dedicated to the trajectory tracking control of under-actuated unmanned surface vessel (USV) with state and input quantization. In terms of kinematics, a distributed guidance law is introduced to track the time-varying trajectory of USV. In terms of kinetics, a fuzzy adaptive quantization control method rooted in an event-triggered mechanism is proposed. Uncertainties within the ship model are estimated through a fuzzy logic system, and a linear analytical model is employed to elucidate the input quantization process. This approach reduces the actuator's execution frequency, alleviates the communication burden and conserves valuable communication resources. The boundedness of the internal signals and quantization errors in the closed-loop system are substantiated by presenting some theoretical lemmas. Furthermore, the stability of the proposed control strategy is proved through Lyapunov stability theory. Finally, the effectiveness and feasibility of the proposed fuzzy adaptive quantization control strategy based on event-triggered mechanism are verified by simulation experiments.

Autonomous ships or Unmanned Surface Vehicles (USV) collision avoidance and path planning problems among multi-vessels are investigated in this paper. Firstly, a modified fuzzy dynamic risk of collision model based on time and space collision risk index is proposed, which is much closer to real ship applications. Then, the fitness functions based on the risk of collision, navigational economy, International Regulations for Preventing Collisions at Sea 1972 (COLREGs) and collision avoidance timing are established respectively to ensure the rationality of ship collision avoidance decisions. Moreover, path planning with global search capability is realized by the multi-objective decision theory combined with a genetic algorithm. The practicability and rationality of the recommended trajectory are guaranteed. Meanwhile, the problem of the non-inferior solution can be addressed by adapting the weight method and the constraint method and the optimized solution of the decision-making system can be achieved finally. Simulation results are further presented to validate the effectiveness of the proposed path planning and collision avoidance methods.

For the maritime environment with limited communication bandwidth at sea, this paper investigates the distributed unmanned surface vehicles (USVs) formation tracking control problem with input and state quantization based on a time-varying threshold event-triggered mechanism. While ensuring that each follower USV can quickly form a formation and follow the virtual leader on the desired trajectory, it reduces the execution frequency of the controller, decreases the control amplitude, and saves communication resources. Firstly, a distributed event-triggered extended state observer (ESO) is designed to observe the virtual leader’s position information, and another ESO is used to estimate the quantized state information of each USV and the uncertainty terms existing in the model. Subsequently, the kinematic guidance law and the kinetic control laws based on a time-varying threshold event-triggered mechanism are proposed in quantized environment using the hierarchical design approach. In addition, the linear splitting of the input quantization makes the designed controller no longer require a priori information on the quantization parameters. Finally, the stability of the designed distributed USV formation tracking control system with input and state quantization based on the time-varying threshold event-triggered mechanism is demonstrated by input-to-state stability theory, and the absence of Zeno behavior in the proposed control strategy is also verified. Three sets of simulation experiments demonstrate the effectiveness and scientific validity of the proposed method.

This paper concerned with the network observer based adaptive trajectory tracking control strategy of Unmanned Surface Vehicle with event-triggered mechanisms and signal quantization. In expound upon input quantization, this paper introduces a linear analytical model enabling controller design without necessitating prior knowledge of the input quantization parameters. Meanwhile, the quantized state variables are estimated through the neural network-based observer. As a result, the quantized feedback controller is designed to use the observer's estimation results, through a combination of backstepping, dynamic surface techniques, and event-triggered mechanisms. The stability of the formulated closed-loop system is demonstrated through the application of Lyapunov stability theory principles. Ultimately, the effectiveness of the proposed control strategy is substantiated through simulation experiments.