Model-Free Distributed Consensus Control Based on Actor–Critic Framework for Discrete-Time Nonlinear Multiagent Systems

Wang, Wei; Chen, Xin; Fu, Hao; Wu, Min

doi:10.1109/tsmc.2018.2883801

Cited by 45 publications

(25 citation statements)

References 43 publications

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“…Generally, path planning can be regarded as the multiobjective optimization [31] and local information interaction [32], [33] problems. In the process of crowd evacuation, an individual's action decision [34], [35], [36], [37], [38], [39], [40] is dependent on the evacuating directions of nearby agents, the locations of hazards, and the obstacles in the scene.…”

Section: Crowd Path Planningmentioning

confidence: 99%

Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations

Xie

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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Numerous research efforts have been conducted to simulate crowd movements, while relatively few of them are specifically focused on multi-hazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multi-hazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and nonconcurrent, static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an Emotional Reciprocal Velocity Obstacles (ERVO) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experiment results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multi-hazard environments.

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Section: Crowd Path Planningmentioning

confidence: 99%

Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations

Xie

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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“…In [28], a new VI data-driven algorithm for nonlinear control systems is studied. In [29][30][31][32], online and off line data-driven algorithms combining Actor and critic neural networks are designed to achieve approximate optimal and optimal control. In [33] and [34], distributed observer and output regulation theory are used to design a data-driven algorithm that can achieve output consensus.…”

Section: Introductionmentioning

confidence: 99%

“…These results are clearly effective and useful. However, summarized from the literatures [29][30][31][32] that the existing results need to collect all data of agents to implement data-driven algorithms, Which means that the amount of calculation is huge.…”

Section: Introductionmentioning

confidence: 99%

Model‐free distributed optimal control for continuous‐time linear systems

Feng

Zhao

2022

IET Control Theory & Appl

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This paper studies the distributed optimal consensus problem for a group of continuous‐time linear systems in the scenario that the dynamics of linear systems are completely unknown. Both an optimal state feedback control law and an optimal output feedback control law are constructed by solving the Algebraic Riccati Equation (ARE) that contains no global information, which guarantees the consensus of linear systems and the global optimality. Adaptive Dynamic Programming (ADP) method is presented to solve the ARE, thus the control algorithms are not relying on the dynamics of linear systems any more. Finally, numerical simulation results are presented to demonstrate the efficiency of proposed approach.

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“…However, the concurrent learning of multi-zone controllers causes the overall operating environment to be no longer stable. Such learning algorithms lack consistent gradient signals in multiregion power grids [24]; therefore, they cannot guarantee frequency control performance in a cooperative manner.…”

Section: Introductionmentioning

confidence: 99%

Convex Neural Networks Based Reinforcement Learning for Load Frequency Control under Denial of Service Attacks

Zeng

Zhu

et al. 2022

Algorithms

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With the increase in the complexity and informatization of power grids, new challenges, such as access to a large number of distributed energy sources and cyber attacks on power grid control systems, are brought to load-frequency control. As load-frequency control methods, both aggregated distributed energy sources (ADES) and artificial intelligence techniques provide flexible solution strategies to mitigate the frequency deviation of power grids. This paper proposes a load-frequency control strategy of ADES-based reinforcement learning under the consideration of reducing the impact of denial of service (DoS) attacks. Reinforcement learning is used to evaluate the pros and cons of the proposed frequency control strategy. The entire evaluation process is realized by the approximation of convex neural networks. Convex neural networks are used to convert the nonlinear optimization problems of reinforcement learning for long-term performance into the corresponding convex optimization problems. Thus, the local optimum is avoided, the optimization process of the strategy utility function is accelerated, and the response ability of controllers is improved. The stability of power grids and the convergence of convex neural networks under the proposed frequency control strategy are studied by constructing Lyapunov functions to obtain the sufficient conditions for the steady states of ADES and the weight convergence of actor–critic networks. The article uses the IEEE14, IEEE57, and IEEE118 bus testing systems to verify the proposed strategy. Our experimental results confirm that the proposed frequency control strategy can effectively reduce the frequency deviation of power grids under DoS attacks.

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Model-Free Distributed Consensus Control Based on Actor–Critic Framework for Discrete-Time Nonlinear Multiagent Systems

Cited by 45 publications

References 43 publications

Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations

Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations

Model‐free distributed optimal control for continuous‐time linear systems

Convex Neural Networks Based Reinforcement Learning for Load Frequency Control under Denial of Service Attacks

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