This article studies the model-based event-triggered control (ETC) for the tracking activity of the underactuated surface vessel (USV). Following this ideology, the continuous acquisition of states is no longer needed, and the communication traffic is reduced in the channel of sensor to controller. The control laws are fabricated in the frame of an adaptive model, which is renewed with the states of the original system whenever the triggering condition is violated. In the scheme, both internal and external uncertainties are approximated by the neural networks (NNs). To decrease the computing complexity, the minimum learning parameters (MLPs) are involved both in the adaptive model and the derived controller. The adaptive laws of only two MLPs are devised, and their updating only happens at triggering instants. Using the MLPs, an adaptive triggering condition is further derived. To avoid the ``Zeno'' phenomenon in small tracking errors, a dead-zone operator is designed for the triggering condition. Furthermore, we incorporate the dynamic surface control (DSC) into the controller design, such that the jumping of virtual control laws at triggering instants is smoothed and the problem of ``complexity explosion'' is circumvented. Through the techniques of the impulsive dynamic system and the direct Lyapunov function, the parameter setting for the DSC is derived to guarantee the semiglobal uniformly ultimate boundedness (SGUUB) of all the error signals in the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through the simulation.

The backstepping control is a kind of nonlinear controller design algorithm. To simplify the nonlinear controller design process, decrease the number of undetermined parameters, improve the robustness, and reduce the energy consumption of the course-keeping controller for ships, an improved concise design method is proposed by introducing an arctan nonlinear function passed through by course error signal under the backstepping design framework, while the design process of the controller is simplified to only one step. The simulation results indicate that, compared with the backstepping-based controller, the maximum response performance (quantified by the mean absolute error) under the proposed controller increases by 57.8%, the maximum energy cost performance (quantified by the mean integral absolute) reduces by 28.6% and the maximum smoothness performance (quantified by the mean total variation) reduces by 44.3% with the strong ability of disturbances rejection. The algorithm given in the note has advantages of the simple design process, strong robustness, and low energy consumption.

Numerous researches have been done to develop ASV (Autonomous Surface Vessel) collision avoidance systems. Most of the systems used static methods but did not apply a knowledge base where solutions can be reused and adapted to solve a new case. In this paper, an algorithm of autonomous collision avoidance is proposed considering steering dynamic for ASV. The process of this learning method is to recall the FCBR (Fuzzy Case Base Reasoning) containing basic expert knowledge in the form of stored cases. The solutions will be retrieved from the knowledge base to find a NH (New Heading) command for collision avoidance. Moreover, to execute the NH, a design of adaptive fuzzy ASV heading control system based on command filter is conducted considering the input saturation constraints and external disturbances. T-S fuzzy logic is employed to approximate nonlinear uncertainties existing in the heading control system adopting the MLP (Minimal Learning Parameter) technique. Finally, simulations prove that the method is effective to retrieve the past similar cases for the new collision avoidance situation and give its solution for ASV to track adjusted heading.

This paper concerns non-fragile-observer-based integral sliding mode control for a class of uncertain switched hyperbolic systems (USHSs). Firstly, by means of variable substitutions, the original hyperbolic differential system is reduced into a first-order one. Then, a non-fragile observer and the state-estimate-based sliding mode control law are designed respectively, such that the reachability of the sliding surface and the novel criteria on asymptotical stability of the sliding dynamic USHSs can be ensured. Finally, a numerical example is presented to illustrate the effectiveness of our findings.

To solve the nonlinear control problems of the unknown time-varying environmental disturbances and parametric uncertainties for ship course-keeping control, this paper presents an adaptive self-regulation PID (APID) scheme which can ensure the boundedness of all signals in the ship course-keeping control system by using the Lyapunov direct method. Compared with the traditional PID control scheme, the APID control scheme not only is independent of the model parameters and the unknown input, but also can regulate the gain of PID adaptively and resist time-varying disturbances well. Simulation results illustrate the effectiveness and the robustness of the proposed control scheme.