Iterative learning control approach for a kind of heterogeneous multi-agent systems with distributed initial state learning

Li, Jinsha; Li, Junmin

doi:10.1016/j.amc.2015.06.035

Cited by 30 publications

(28 citation statements)

References 28 publications

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“…Consensus was also achieved in the work of Peng et al, where a distributed iterative learning control algorithm, combined with state‐feedback and output‐feedback techniques, is used to achieve tracking of a leader agent in MAS with a communication network characterized by a directed graph containing a spanning tree. An iterative learning‐based approach was also considered in the work of Li and Li, where a MAS with heterogeneous agents achieves consensus to a reference signal related to a leader agent. In this work, a distributed initial state learning mechanism is incorporated, relaxing the common assumption of identical initial condition used in the literature of ILC.…”

Section: Main Control Objectives Found In the Literature Of Olc In Masmentioning

confidence: 99%

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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Section: Main Control Objectives Found In the Literature Of Olc In Masmentioning

confidence: 99%

Hybrid online learning control in networked multiagent systems: A survey

Poveda

Benosman

Teel

2018

Adaptive Control & Signal

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“…Iterative learning control methodology, which is proposed by Arimoto et al in 1984 (See [1]), is to utilize the previous control information of the studied systems. The repetitive behavior has been a major research area and a hot issue in recent years (See [2][3][4][5][6][7][8][9][10][11][12][13][14][15]).…”

Section: Introductionmentioning

confidence: 99%

The Application of Zhang-Gradient Method for Iterative Learning Control

Zhang¹

2020

JAAM

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“…Yang et al [15] proposed an ILC algorithm to solve the consensus tracking problems of homogeneous and heterogeneous multiagent systems, respectively, and the output consensus conditions have been obtained based on the concept of graph-dependent matrix norm. Li [16] considered a heterogeneous multiagent system composed of first-and second-order dynamics, and the leader was assumed to have second-order dynamics. Different protocols have been designed for the heterogeneous following agents, so that all the following agents tracked the state of the leader asymptotically [16].…”

Section: Introductionmentioning

confidence: 99%

“…Li [16] considered a heterogeneous multiagent system composed of first-and second-order dynamics, and the leader was assumed to have second-order dynamics. Different protocols have been designed for the heterogeneous following agents, so that all the following agents tracked the state of the leader asymptotically [16].…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking by Iterative Learning Control for Linear Heterogeneous Multiagent Systems Based on Fractional-Power Error Signals

Luo

Liu

2019

Algorithms

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This paper deals with the consensus tracking problem of heterogeneous linear multiagent systems under the repeatable operation environment, and adopts a proportional differential (PD)-type iterative learning control (ILC) algorithm based on the fractional-power tracking error. According to graph theory and operator theory, convergence condition is obtained for the systems under the interconnection topology that contains a spanning tree rooted at the reference trajectory named as the leader. Our algorithm based on fractional-power tracking error achieves a faster convergence rate than the usual PD-type ILC algorithm based on the integer-order tracking error. Simulation examples illustrate the correctness of our proposed algorithm.

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Iterative learning control approach for a kind of heterogeneous multi-agent systems with distributed initial state learning

Cited by 30 publications

References 28 publications

Hybrid online learning control in networked multiagent systems: A survey

Hybrid online learning control in networked multiagent systems: A survey

The Application of Zhang-Gradient Method for Iterative Learning Control

Consensus Tracking by Iterative Learning Control for Linear Heterogeneous Multiagent Systems Based on Fractional-Power Error Signals

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