Model-Free Cooperative Control for Multi-Agent Systems Using the Approximate Dynamic Programming Approach

Qu, Yanhua; Liu, Jinglu

doi:10.1109/access.2018.2849754

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Recent developments in autonomous vehicles have seen the coordinated control of MAS being applied to unmanned aerial vehicles, robots and aircraft [21]. Current techniques for the coordinated control of MAS include dynamic programming [22], fuzzy-based model [23], model predictive control [24]- [26], machine learning [27]- [29].…”

Section: Coordinated Control Of Masmentioning

confidence: 99%

Adaptive Regret Minimization for Learning Complex Team-Based Tactics

et al. 2019

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This paper presents an approach and analysis for performing decentralized cooperative control of a team of decoys to achieve the Honeypot Ambush tactic. In this tactic, the threats are successfully lured into a designated region where they can be easily defeated. The decoys learn to cooperate by incorporating a game-theory-based online-learning method, known as regret minimization, to maximize the team's global reward. The decoy agents are assumed to have physical limitations and to be subject to certain stringent range constraints required for deceiving the networked threats. By employing an efficient coordination mechanism, the agents learn to be less greedy and allow weaker agents to catch up on their rewards to improve team performance. Such a coordination solution corresponds to achieving convergence to coarse correlated equilibrium. The numerical results verify the effectiveness of the proposed solution to achieve a global satisfaction outcome and to adapt to a wide spectrum of scenarios.

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Section: Coordinated Control Of Masmentioning

confidence: 99%

Adaptive Regret Minimization for Learning Complex Team-Based Tactics

et al. 2019

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“…In recent years, adaptive dynamic programming (ADP) has been employed to solve nonlinear optimal control problems [3]- [9]. The significant advantage of ADP is that the controller is less dependent on the mathematical model of the system.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Optimal Tracking Control for Natural Gas Desulfurization System

Zhou

Liu

et al. 2019

IEEE Access

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Desulfurization control of natural gas has long been a challenging industrial issue owing to its inherent difficulty in establishing accurate mathematical model for the nonlinear and strong coupling process. In this paper, a data-based adaptive dynamic programming (ADP) algorithm is presented to solve optimal control for natural gas desulfurization. First, neural network (NN) is used to reconstruct the dynamics of the desulfurization system via the input and output production data. Then, an improved unscented Kalman filter (IUKF) aided ADP method is presented to solve optimal control problem for desulfurization system, where IUKF algorithm is developed as a new weight-updating strategy for the action network and the critic network. The IUKF aided algorithm can improve the convergence speed as well as the anti-interference ability of the ADP controller. Furthermore, the proposed IUKF-ADP algorithm is implemented using the heuristic dynamic programming (HDP) structure. Finally, the effectiveness of the proposed IUKF-ADP algorithm is demonstrated through experiments of the natural gas desulfurization system. INDEX TERMS Adaptive dynamic programming (ADP), unscented Kalman filter (UKF), data-based, optimal control, desulfurization.

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Model-free H∞ synchronization of leader–follower systems with guaranteed convergence rate using reinforcement learning

Rahdarian

Shamaghdari

2022

Int. J. Dynam. Control

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Model-Free Cooperative Control for Multi-Agent Systems Using the Approximate Dynamic Programming Approach

Cited by 7 publications

References 32 publications

Adaptive Regret Minimization for Learning Complex Team-Based Tactics

Adaptive Regret Minimization for Learning Complex Team-Based Tactics

Data-Based Optimal Tracking Control for Natural Gas Desulfurization System

Model-free H∞ synchronization of leader–follower systems with guaranteed convergence rate using reinforcement learning

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