Adaptive control for multi-agent systems with actuator fault via reinforcement learning and its application on multi-unmanned surface vehicle
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Adaptive control for multi-agent systems with actuator fault via reinforcement learning and its application on multi-unmanned surface vehicle
Authors
Keywords
-
Journal
Ocean Engineering
Volume 280, Issue -, Pages 114545
Publisher
Elsevier BV
Online
2023-04-25
DOI
10.1016/j.oceaneng.2023.114545
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Numerical evaluation of a ship's manoeuvrability and course keeping control under various wave conditions using CFD
- (2021) Daejeong Kim et al. OCEAN ENGINEERING
- Distributed Path Following of Multiple Under-Actuated Autonomous Surface Vehicles Based on Data-Driven Neural Predictors via Integral Concurrent Learning
- (2021) Lu Liu et al. IEEE Transactions on Neural Networks and Learning Systems
- Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections
- (2021) Ben Niu et al. IEEE-CAA Journal of Automatica Sinica
- Adaptive synchronization for surface vessels with disturbances and saturated thruster dynamics
- (2020) Xin Hu et al. OCEAN ENGINEERING
- Intermittent Control for Quasisynchronization of Delayed Discrete-Time Neural Networks
- (2020) Sanbo Ding et al. IEEE Transactions on Cybernetics
- Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm
- (2019) Zhinan Peng et al. INFORMATION SCIENCES
- Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems With Actuator Faults via Integral Reinforcement Learning Method
- (2019) Huaguang Zhang et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Multigradient recursive reinforcement learning NN control for affine nonlinear systems with unmodeled dynamics
- (2019) Weiwei Bai et al. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
- Neural Networks-Based Distributed Adaptive Control of Nonlinear Multiagent Systems
- (2019) Qikun Shen et al. IEEE Transactions on Neural Networks and Learning Systems
- Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems
- (2019) Guoxing Wen et al. INFORMATION SCIENCES
- Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment
- (2019) Haoran Li et al. IEEE Transactions on Neural Networks and Learning Systems
- Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation
- (2019) Weiwei Bai et al. IEEE Transactions on Cybernetics
- Continuous-Time Time-Varying Policy Iteration
- (2019) Qinglai Wei et al. IEEE Transactions on Cybernetics
- GrHDP Solution for Optimal Consensus Control of Multiagent Discrete-Time Systems
- (2018) Xiangnan Zhong et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Convergence Analysis
- (2018) Qinglai Wei et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Distributed Consensus Control of One-Sided Lipschitz Nonlinear Multiagent Systems
- (2018) Muhammad Rehan et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Global Robust Adaptive Trajectory Tracking Control for Surface Ships Under Input Saturation
- (2018) Guibing Zhu et al. IEEE JOURNAL OF OCEANIC ENGINEERING
- Adaptive Fuzzy Leader-Following Consensus Control for Stochastic Multiagent Systems with Heterogeneous Nonlinear Dynamics
- (2017) Chang-E Ren et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults
- (2017) Yongming Li et al. IEEE Transactions on Neural Networks and Learning Systems
- An energy optimal thrust allocation method for the marine dynamic positioning system based on adaptive hybrid artificial bee colony algorithm
- (2016) Defeng Wu et al. OCEAN ENGINEERING
- Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity
- (2015) Zhi Liu et al. IEEE Transactions on Neural Networks and Learning Systems
- Distributed consensus of linear multi-agent systems with adaptive dynamic protocols
- (2013) Zhongkui Li et al. AUTOMATICA
- Observer-Based Adaptive Decentralized Fuzzy Fault-Tolerant Control of Nonlinear Large-Scale Systems With Actuator Failures
- (2013) Shaocheng Tong et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Cooperative Adaptive Fuzzy Tracking Control for Networked Unknown Nonlinear Multiagent Systems With Time-Varying Actuator Faults
- (2013) Qikun Shen et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Multi-agent differential graphical games: Online adaptive learning solution for synchronization with optimality
- (2012) Kyriakos G. Vamvoudakis et al. AUTOMATICA
- Model Predictive Control Schemes for Consensus in Multi-Agent Systems with Single- and Double-Integrator Dynamics
- (2009) G. Ferrari-Trecate et al. IEEE TRANSACTIONS ON AUTOMATIC CONTROL
- Reinforcement-Learning-Based Output-Feedback Control of Nonstrict Nonlinear Discrete-Time Systems With Application to Engine Emission Control
- (2009) P. Shih et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
- Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks
- (2009) Zeng-Guang Hou et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
- A DSC Approach to Robust Adaptive NN Tracking Control for Strict-Feedback Nonlinear Systems
- (2009) Tie-Shan Li et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started