Straight-Path Following and Formation Control of USVs Using Distributed Deep Reinforcement Learning and Adaptive Neural Network

Han, Zhizhong; Wang, Yintao; Sun, Qi

doi:10.1109/jas.2023.123255

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…In addition, the work in this paper can be extended to MASs formation control under various attacks, such as [41]. In the future, we will try to combine this work with the neural network method [42] to study the formation control of MASs.…”

Section: Discussionmentioning

confidence: 99%

Fixed-Time Formation Tracking Control of Nonlinear Multi-Agent Systems with Directed Topology and Disturbance

2023

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In this paper, we study the fixed-time formation (FixF) control problem for the nonlinear second-order multi-agent systems (MASs) with directed graph, where all agents are subject to communication disturbances. To overcome the bounded disturbance of communication and to guarantee the realization of predesignated formation within a fixed-time, a suitable FixF control protocol based on the backstepping method is proposed. Furthermore, to eliminate the dependence of the control parameters on global information, a new adaptive FixF control protocol is provided to combine with some mild conditions of control gain such that the formation with acceptable bounded error of MASs can be achieved within a fixed-time. Finally, the theoretical results are verified by two simulation examples, one of the simulation examples is that we set the desired formation as a regular octagon and the desired formation motion trajectory as a circle, the results show that all agents can form formation motion within a fixed-time.

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Section: Discussionmentioning

confidence: 99%

Fixed-Time Formation Tracking Control of Nonlinear Multi-Agent Systems with Directed Topology and Disturbance

2023

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“…In [32], a DRL controller is designed using the DDPG for path following, and simulation shows that the proposed method is better than the PID in terms of transient characteristics. In [33], a distributed DRL method is proposed to solve the path-following control of an under-actuated AMV, where the DDPG-based controller is designed and the radial basis neural network is utilized to approximate the unknown disturbances. In [34], an improved DDPG control method was proposed for path following based on an optimized sampling pool and average motion evaluation network, and the simulation results show that the proposed method effectively improves the utilization rate of samples and avoids falling into a local optimum in the training process.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Path-Following Control Scheme for an Uncertain Under-Actuated Autonomous Marine Vehicle

Qu,

Jiang,

Zhang

et al. 2023

JMSE

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In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight guidance, a surge-heading joint guidance method is developed within the kinematic level, thereby enabling the AMV to follow the desired path accurately. Within the dynamic level, model uncertainties and time-varying environment disturbances are taken into account, and the reinforcement learning control method using the twin-delay deep deterministic policy gradient (TD3) is developed for the under-actuated vehicle, where path-following actions are generated via the state space and hybrid rewards. Additionally, actor-critic networks are developed using the long-short time memory (LSTM) network, and the vehicle can successfully make a decision by the aid of historical states, thus enhancing the convergence rate of dynamic controllers. Simulation results and comprehensive comparisons on a prototype AMV demonstrate the remarkable effectiveness and superiority of the proposed LSTM-TD3-based path-following control scheme.

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Event-triggered model-free adaptive sliding-mode heading control for unmanned surface vehicles under DoS attacks

Chen,

Zhao,

et al. 2024

Applied Ocean Research

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Straight-Path Following and Formation Control of USVs Using Distributed Deep Reinforcement Learning and Adaptive Neural Network

Cited by 12 publications

References 15 publications

Fixed-Time Formation Tracking Control of Nonlinear Multi-Agent Systems with Directed Topology and Disturbance

Fixed-Time Formation Tracking Control of Nonlinear Multi-Agent Systems with Directed Topology and Disturbance

A Deep Reinforcement Learning-Based Path-Following Control Scheme for an Uncertain Under-Actuated Autonomous Marine Vehicle

Event-triggered model-free adaptive sliding-mode heading control for unmanned surface vehicles under DoS attacks

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