Chenfeng Huang

The study addresses trajectory tracking control for underactuated vessels with uncertain backlash-type hysteresis. First, an improved practical L1-based guidance strategy is developed by embedding the L1 mechanism into the virtual ship framework to eliminate steering overshoot and yaw angle error accumulation, which can facilitate the smooth turning of ships along waypoint-based paths with large curvature. Next, to mitigate control performance degradation induced by backlash-like hysteresis nonlinearity, an improved quadratic function is utilized to boost the closed-loop system’s convergence capability. Moreover, system model uncertainty-induced perturbations are compensated using the resilient neural damping method, which can simplify the structure and reduce the computation burden of the proposed controller. Utilizing Lyapunov-based approaches and the special Young’s inequality, uniformly ultimately bounded stability over a semi-global domain is established. Finally, numerical simulations are executed to validate the efficacy of the developed control architecture.

In this article, a robust adaptive dynamic position-control problem is addressed for full-actuated surface vessels under coupled uncertainties from unmodeled hydrodynamic effects and time-varying external disturbances. To obtain a high-performance dynamic position controller, a reinforcement learning (RL) weights law involving actor and critic networks is designed without knowledge of the model dynamics and the disturbance parameters. This can enhance the robustness of the closed-loop control system. Furthermore, dynamic surface control is integrated to diminish the design complexity resulting from the derivative of the kinematics, while ensuring semi-global uniformly ultimately bounded (SGUUB) stability through Lyapunov-based synthesis. Simulations are carried out to evaluate the superiority and feasibility of the proposed algorithm.

To improve the autonomy of marine ranching operations, this paper addresses the cooperative formation control and multi-task switching problem of ranch autonomous marine vehicles (AMVs) with the structure synchronized dynamic event-triggered mechanism (DETM). In the proposed algorithm, adaptive potential ship (APS) technique is adopted to guarantee the integrity and continuity of the guidance signal. Combined with the guidance principle, a cooperative formation control algorithm is proposed by employing the DETM and neural networks (NNs). The communication burden in the channel from the sensor to the controller and from the controller to actuator has been reduced for the merits of the proposed DETM. Unlike the existing results, the proposed DETM can activate the threshold parameters, adaptive parameters and NNs weight estimators at the triggering times synchronously. This releases the computation burden greatly. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded (SGUUB) stability via the Lyapunov theorem. Finally, two simulations consist of the marine ranching path following and comparative example are carried out to evaluate the advantages of the proposed strategy.