Distributed Resilient Observer-Based Fault-Tolerant Control for Heterogeneous Multiagent Systems Under Actuator Faults and DoS Attacks

Deng, Chao; Chen, Wen

doi:10.1109/tcns.2020.2972601

Cited by 219 publications

(77 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a sub-field of AI, MAS has excellent portability. Therefore, MAS has been widely adopted to model systems with multi-participators, which are widespread in numerous fields including communication protocol [49], cooperative control [50]- [51], fault-tolerant control [52], electrical power system [53], sensor deployment [54], transportation [55]- [57], etc. MAS researchers focus on structural mechanisms of complex systems.…”

Section: B Multi-agent Reinforcement Learning (Marl)mentioning

confidence: 99%

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

Pan

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The Dynamic Traffic Assignment (DTA) is one of the important measures to alleviate urban network traffic congestion. The congestions are usually caused by stochastic traffic demands, which are generally unassignable from time dimension in the real-world but are assumed to be assignable in existing DTA methods (i.e. real-time travel demands). In this paper, a distributed DTA method for preventing urban network traffic congestion caused by stochastic real-time travel demands by improving Multi-Agent Reinforcement Learning (MARL). A team structure, which consists of decision-makers and advisers, is designed to learn parallelly in realistic DTA tasks. To reduce the size of the solution space adaptively, the dynamic critical values advised by adviser agents are adopted as constraints for the strategy space of decision-makers (i.e. main agents). A collaborative heterogeneous-adviser mechanism is designed to avoid deviation of guidance. To enhance the adaptability of DTA to the changeable external environment, the mixed strategy concept is introduced to improve the decision-making process of main agents. The respective mapping mechanisms are designed to define adaptive learning rates to improve the sensitivity of MARL. The Sioux Falls (SF) network is established as a test platform via a Dynamic Network Loading (DNL). The effectiveness of the suggested DTA method is assessed through numerical simulations SF network. Under the influence of the scenario with stochastic real-time travel demands, the results show that the proposed method outperforms in terms of the throughput of the network and the individual average travel time among the overall network. Additionally, the ability of the proposed method in response to the external environment rapidly has also been demonstrated. Adopting the suggested method can improve the state of the art to assign stochastic real-time travel demands dynamically and to avoid potential traffic congestion fundamentally. INDEX TERMS dynamic traffic assignment, intelligent transportation system, multi-agent system, reinforcement learning, multi-agent reinforcement learning, numerical simulation.

show abstract

Section: B Multi-agent Reinforcement Learning (Marl)mentioning

confidence: 99%

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

Pan

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The front power chain includes three power sources-the engine-generator set, power battery pack, and supercapacitor-which are used to convert fossil into electric energy and then store it. The rear power chain includes multiple drive motors to enable the mutual conversion between electric and mechanical energy [18][19][20][21]. The structure of a hybrid electric system is shown in Fig.…”

Section: Heavy-duty Hybrid Electric Vehicle Modelingmentioning

confidence: 99%

“…, (21) where F, δ, m, v, Cd, Af, μ, g, and α denote the total driving force between the tire and the ground, equivalent rotational coefficient, vehicle mass, vehicle speed, air resistance coefficient, windward surface area of the vehicle, rolling resistance coefficient, gravitational acceleration, and vehicle gradient angle, respectively. The power balance equation at the tire becomes…”

Section: F Modeling Vehicle Dynamicsmentioning

confidence: 99%

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Chen¹,

Liao²,

Ma³

2020

IEEE Access

View full text Add to dashboard Cite

“…In view of its remarkable application prospect, numerous intelligent control strategies have been proposed such as tracking control, [1][2][3] consensus control, 4,5 containment control, [6][7][8] and so on. 9 Among these control approaches, the containment control is regarded as a popular method because it is more general and can be converted into tracking control by setting only one leader. Hence, the containment control is an attractive control scheme for MASs.…”

Section: Introductionmentioning

confidence: 99%

“…Until now, it has been applied in many industry and military fields such as distributed robotic networks, attitude of astrovehicle alignment, multivehicle systems, unmanned aerial vehicles, and so on. In view of its remarkable application prospect, numerous intelligent control strategies have been proposed such as tracking control, 1‐3 consensus control, 4,5 containment control, 6‐8 and so on 9 . Among these control approaches, the containment control is regarded as a popular method because it is more general and can be converted into tracking control by setting only one leader.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Zhang

Dong

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article investigates the sampled-data containment control issue for nonlinear multiagent systems (MASs) with packet losses in the framework of Takagi-Sugeno fuzzy model. In the process of designing fuzzy sampled-data containment controller, a crucial factor, that is, asynchronous premise variables, is tackled via the proposed novel asynchronous premise rebuilding approach. Based on the fact that there may be packet losses in communication networks, the fuzzy sampled-data MAS is concerned as a switched version with two subsystems. Then, in the light of average dwell-time method and Lyapunov stability theory, sufficient conditions of designing fuzzy sampled-data containment controller are derived such that the states containment can be achieved for considered systems in spite of packet losses. Finally, both a numerical simulation and a Caltech multivehicle wireless test bed simulation are presented to confirm the effectiveness of the proposed control scheme.

show abstract

Distributed Resilient Observer-Based Fault-Tolerant Control for Heterogeneous Multiagent Systems Under Actuator Faults and DoS Attacks

Cited by 219 publications

References 37 publications

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Contact Info

Product

Resources

About