Distributed adaptive consensus control of heterogeneous multi‐agent chaotic systems with unknown time delays

Cui, Bing; Ma, Tiedong; Lewis, Frank L.; Zhao, Chunhui; Song, Yongduan; Feng, Chi

doi:10.1049/iet-cta.2015.0187

Cited by 31 publications

(32 citation statements)

References 42 publications

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“…In the related works [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], the cooperative and competitive relationship between the Complexity 3 agents and is described as − and + , respectively. In [25,26], they also are presented by the coupling weight between these two agents.…”

Section: Resultsmentioning

confidence: 99%

“…In [12,13], to the heterogeneous systems with undirected topology, the consensus and the globally bounded consensus problems were studied, respectively. In [14][15][16], the authors discussed the synchronization and consensus issues for heterogeneous systems with the influence of time delays, respectively. Meanwhile, the consensus problems for the discrete-time heterogeneous systems were studied in [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…As known, time delays inevitably exist in the complex systems. Meanwhile, they usually affect and even destroy the stability of complex systems [14][15][16]. Therefore, it is very important and necessary to discuss the group consensus problems with the influence of time delays.…”

Section: Introductionmentioning

confidence: 99%

“…Our contributions are mainly listed as below: firstly, two novel delayed group consensus protocols are proposed, which is modeled by the agents' competitive relationship. The innovation is mainly embodied in the following two aspects: on the one hand, the related works [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] are based on the cooperative complex systems. On the other hand, there is a special case in the works [15,[21][22][23]; that is, the dynamics of the first-order agents are added with a virtual velocity estimation in order to make theoretical analysis more convenient.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

2017

Complexity

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This paper discusses the couple-group consensus problems for a class of heterogeneous multiagent networks including the following two cases: with communication and input time delays, respectively. Different from the related cooperative networks, two novel delayed group consensus protocols are designed based on the competitive relationship between the agents. Furthermore, we absolutely relax the in-degree balance and other restrictive preconditions which existed in the relevant works. Some sufficient algebraic criteria for the achievement of couple-group consensus and the upper bound of the input time delays are technically obtained via the frequency domain method and matrix theory, respectively. The results show that the achievement of the couple-group consensus depends on the second-order agents' in-degree and the control parameters of the systems, whereas it is independent of the communication time delays. Meanwhile, the upper bound of the input time delay is determined by the control parameters and the in-degree of the first-order agents. Finally, the validity of the proposed results is verified by several simulated examples.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

2017

Complexity

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“…By the help of neural network (NN) or fuzzy logic system (i.e. its good universal approximation property), many significant results for the synchronisation of nonlinear multi-agent systems have been developed (Chen, Wen, Liu, & Wang 2014;Cheng, Hou, Tan, Lin, & Zhang, 2010;Cui et al, 2015;Das & Lewis, 2010Peng, Wang, Zhang, & Sun, 2014;Peng, Wang, Zhang, Sun, & Wang, 2013;Shen, Jiang, Shi, & Zhao, 2014;Shen, Shi, & Shi, 2015;Zhang & Lewis, 2012). Hou et al (2009) proposed a robust adaptive control approach to solve the leaderless consensus problem of networks of agents with the uncertain nonlinear dynamics and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

Cui

Zhao

et al. 2016

International Journal of Systems Science

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In this paper, the cooperative adaptive consensus tracking problem for heterogeneous nonlinear multiagent systems on directed graph is addressed. Each follower is modelled as a general nonlinear system with the unknown and nonidentical nonlinear dynamics, disturbances and actuator failures. Cooperative fault tolerant neural network tracking controllers with online adaptive learning features are proposed to guarantee that all agents synchronise to the trajectory of one leader with bounded adjustable synchronisation errors. With the help of linear quadratic regulator-based optimal design, a graph-dependent Lyapunov proof provides error bounds that depend on the graph topology, one virtual matrix and some design parameters.Of particular interest is that if the control gain is selected appropriately, the proposed control scheme can be implemented in a unified framework no matter whether there are faults or not. Furthermore, the fault detection and isolation are not needed to implement. Finally, a simulation is given to verify the effectiveness of the proposed method.

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Hybrid online learning control in networked multiagent systems: A survey

Poveda

Benosman

Teel

2018

Adaptive Control & Signal

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This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous-time dynamical systems and discrete-time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning-based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous-time dynamical systems and discrete-time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems. KEYWORDSadaptive control, hybrid dynamical systems, learning, multiagent systems, networked systems 228One of the fields where control systems with learning and adaptation have recently seen increased attention is in the area of networked multiagent systems (MAS), 16,17 which are systems comprised of multiple interacting subsystems, each subsystem having individual dynamics and computational capabilities as well as limited sensing, communication, and actuation. Depending on the structure of the underlying communication graph between the agents of the system, MAS can be centralized or decentralized. In a centralized MAS, there exists one central agent which has access to the state information of all other agents and which is able to compute the control action for all agents of the system. On the other hand, in a decentralized or distributed MAS, agents individually compute their own control signals, which are shared with a subset of the other agents characterized by a communication graph. Figure 1 illustrates three MASs with different communication graphs. Centralized MASs are typically not scalable, and they present the disadvantage of having a single point of failure that could potentially shut down the complete system. 16 Decentralized and distributed systems, on the other hand, are scalable and more robust, and in nonstationary environments, they allow for adaptation and self-organization of the system. 18 Recent technological advances in communication and computation have made MAS ubiquitous, and today, they can be found in several engineering systems such as in power generation and distribution systems, 19-21 teams of ...

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Distributed adaptive consensus control of heterogeneous multi‐agent chaotic systems with unknown time delays

Cited by 31 publications

References 42 publications

Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

Hybrid online learning control in networked multiagent systems: A survey

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