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. As for the control module, the adaptive event-triggered control algorithm is proposed by employing the robust neural damping technique and the input event-triggered mechanism. And the derived adaptive law can effectively solve perturbations from the gain uncertainty and the external disturbances. Through the theoretical analysis, all signals of the closed-loop control system are with the semi-globally uniform ultimate bounded (SGUUB) stability. The simulation experiments have been presented to verify the obstacle avoidance effectiveness and the burden-some superiority of the algorithm.

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. At first, the SMASS was modeled with the Nomoto model in a simulation waterway. Then, distances, obstacles, and prohibited areas were regularized as rewards or punishments, which were used to judge the performance and manipulation decisions of the vessel Subsequently, improved PPO was introduced to learn the action-reward model, and the neural network model after training was used to manipulate the SMASS's movement. To achieve higher reward values, the SMASS could find an appropriate path or navigation strategy by itself. After a sufficient number of rounds of training, a convincing path and manipulation strategies would likely be produced. Compared with the proposed approach of the existing methods, this approach is more effective in self-learning and continuous optimization and thus closer to human manipulation.

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. An adaptive neural model is established to substitute for the original strict-feedback system and direct the design of the backstepping-based control laws. The states of this adaptive model are reset to the measured states of the original system when the triggering condition is violated. The triggering condition is constructed in the compound form and with the adaptive threshold. The dead-zone operator is involved to avoid the accumulation of triggering instants. In this paper, we notice the problem of "jumps of virtual control laws" for the event-triggered control (ETC) in the backstepping frame, and a detailed formulaic definition is given in section 2.2. To solve this problem, the first-order filters are fabricated to provide the continuous substitutes for virtual control laws. In addition, the "complexity explosion" generated by direct differentiating of virtual control laws can be averted. Through the proposed scheme, the closed-loop system can be viewed as an impulsive dynamic system, and the semi-globally uniformly ultimate boundedness (SGUUB) of all the errors is proved. Finally, two examples validate the feasibility of the proposed control scheme.

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. Then, the off-diagonal inertial matrix is released by restructuring the kinematic loop of the improved model, and the backstepping approach is employed through the control design. To solve the underactuated problem, the tracking error in the sway motion is restructured by adding an adaptive compensating variable and further allocated to the actuated motions. The K∞ functions are structured in the control laws to offset the dynamic disturbances. The Gauss error functions are employed to approximate the uncertainties of the input saturation, which are described by the continuous control inputs with the bounded multiplicative gains. Via the small-gain theorem, the resulting closed-loop system is proved to be ultimately bounded. Finally, a simulation example is carried out to validate the effectiveness of the developed scheme.

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. 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.

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. The developed algorithm could release the internal stability constraints to the unstable plant. By using the graphical stability criterion, a systematic methodology is derived to obtain the exact stabilizing region, where the sole design parameter is related to the stability degree of the closed-loop system. The proposed algorithm is with characteristics of concise and efficient design. Three experiments has been employed to illustrate that the control effects can achieve the satisfied performance in aspects of disturbance rejection and robustness.

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. In the scheme, a control law is designed based on the all-pole Padé approximated model. The unstable process was first mapped into a minimum-phase system, and the actual control is obtained by the closed-loop gain shaping algorithm (CGSA). The advantages are that one has a concise design procedure and can achieve good performance such as disturbance rejection and robustness. Finally, three highly cited examples are used to illustrate the effectiveness of the proposed method.

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. Furthermore, the auto-constructed TMILS scheme is derived for the ship manoeuvring motion identification by combination with a statistic index. Comparing with the existing results, the proposed scheme has the significant computational advantage and is able to estimate the model structure. The illustrative examples demonstrate the effectiveness of the proposed algorithm, especially including the identification application with full-scale trial data.

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. Furthermore, the low frequency learning method improves the applicability of the algorithm. Finally, the comparison experiments with the line-of-sight (LOS) based fuzzy scheme are presented to demonstrate the effectiveness of our results.