LPVS guidance and adaptive event-triggered control for an underactuated surface vessel with the prevention of obstacle's vicious maneuvering.

This paper investigates the adaptive neural event-triggered control for an underactuated surface vessel (USV), considering constraints of the obstacle's vicious maneuvering and the limited communication channel. In the algorithm, a novel logical phantom virtual ship (LPVS) guidance principle is developed to generate the global path following reference and the obstacle avoidance order in the simulation results, where the corresponding operation comply to the suggestion in international regulations for prevention collision at sea (COLREGs). The improved design of velocity obstacle (VO) method can guarantee its predictive capability to prevent the obstacle's vicious maneuvering.

Intelligent Smart Marine Autonomous Surface Ship Decision System Based on Improved PPO Algorithm.

With the development of artificial intelligence technology, the behavior decision-making of an intelligent smart marine autonomous surface ship (SMASS) has become particularly important. This research proposed local path planning and a behavior decision-making approach based on improved Proximal Policy Optimization (PPO), which could drive an unmanned SMASS to the target without requiring any human experiences. In addition, a generalized advantage estimation was added to the loss function of the PPO algorithm, which allowed baselines in PPO algorithms to be self-adjusted.

Adaptive neural tracking control of strict-feedback nonlinear systems with event-triggered state measurement.

This paper investigates the adaptive neural tracking control of the strict-feedback nonlinear systems, where the states are measured in an event-triggered manner so as to save the communication resources. As the neural networks (NNs) account for the unknown dynamics of the system, the minimum learning parameters (MLPs) are extracted from the weights of the NNs and the upper bounds of the disturbances. The estimates of the MLPs are updated in an event-triggered manner to ensure the approximation ability of the NNs and the stability of the closed-loop system.

Adaptive fuzzy tracking control for underactuated surface vessels with unmodeled dynamics and input saturation.

This paper investigates the tracking control of the underactuated surface vessel (USV) with the off-diagonal inertial matrix, and under the influence of unmodeled dynamics as well as the constraint of input saturation. With the fuzzy logic systems (FLSs) accounting for the uncertainties, we present an adaptive fuzzy state-feedback control scheme with the minimum learning parameters (MLPs) of the FLSs. Based on the conventional USV model described in three degrees of freedom (DOF), an improved model is established at first, which involves the terms of dynamic disturbances generated by the unmodeled dynamics of the indecisive motions.

Model-Based Event-Triggered Tracking Control of Underactuated Surface Vessels With Minimum Learning Parameters.

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.

Optimized robust control for industrial unstable process via the mirror-mapping method.

In this work, a novel optimized robust control algorithm, based on the mirror-mapping method, is proposed for a class of industrial unstable process with time delay. The optimizing criterion is to minimize the sensitivity function to enhance its robustness. The controllers are designed based on the Padé approximated mirror-mapping process with a stable form, other than the original unstable system.

Robust controller synthesis for high order unstable processes with time delay using mirror mapping technique.

In this note, a general scheme is proposed for the high order unstable delay process with one or more positive poles, using the mirror mapping technique. The Nyquist criteria is employed to establish a systematic methodology to tune the parameter. The stabilizing parameter region could guarantee the prespecified robustness specification.

Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data.

This research is concerned with the problem of 4 degrees of freedom (DOF) ship manoeuvring identification modelling with the full-scale trial data. To avoid the multi-innovation matrix inversion in the conventional multi-innovation least squares (MILS) algorithm, a new transformed multi-innovation least squares (TMILS) algorithm is first developed by virtue of the coupling identification concept. And much effort is made to guarantee the uniformly ultimate convergence.

A novel DVS guidance principle and robust adaptive path-following control for underactuated ships using low frequency gain-learning.

Around the waypoint-based path-following control for marine ships, a novel dynamic virtual ship (DVS) guidance principle is developed to implement the assumption "the reference path is generated using a virtual ship", which is critical for applying the current theoretical studies in practice. Taking two steerable variables as control inputs, the robust adaptive scheme is proposed by virtue of the robust neural damping and dynamic surface control (DSC) techniques. The derived controller is with the advantages of concise structure and being easy-to-implement for its burdensome superiority.