Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

Zhao, Huarong; Yu, Hongnian

doi:10.1109/access.2020.3014496

Cited by 10 publications

(14 citation statements)

References 44 publications

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“…Thus, the adaptive attack compensator (10) with the attack upper bound estimator (11) can be used to eliminate the deception attack by the distributed filter (9), and the distributed observer (8) can be used accurately estimate the state of the leader.…”

Section: Resultsmentioning

confidence: 99%

“…Content may change prior to final publication. Secondly, by introducing an adaptive compensator (10) with an attack upper bound estimator (11), and use a new distributed filter (9) to filter out the attacks, the position error 0 () i qq  and velocity error 0 () i qq  of the multiple Euler-Lagrange system are shown in Fig. 6, the state of the observer (8) and the leader (2) are shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The cooperative control problems of distributed network systems include synchronization problems, clustering flocking problems, formation control problems, et al In fact, these problems can be considered as the consensus problems, that is, each individual uses limited local information to achieve the same state or output of all individuals. Many consensus control strategies are intensively investigated under various circumstances, for example, adaptive robust control for model uncertainty or system disturbances [6], [7], distributed switching consensus for parameter jump or switching topology [1], [8], adaptive bipartite consensus for cooperation or competition between agents [6], [9], et al However, the realization of the consensus control of the distributed network system depends on the security of information interaction over network communication between the agents. Therefore, it is of great significance to study the consensus problem of distributed network system when the communication channel or manipulation information is attacked, and this problem has also received some substantial research.…”

Section: Introductionmentioning

confidence: 99%

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Leader-Following Consensus of Multiple Euler-Lagrange Systems Under Deception Attacks

et al. 2021

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In this paper, the leader-following consensus problem of a multi-Euler-Lagrange system is studied under deception attacks, where attackers can inject the false data into the data being exchanged over the communication network between two Euler-Lagrange systems. A distributed adaptive filter is proposed to compensate the unknown injection false data, where the magnitude of the false data is estimated. Meanwhile, the performance of the multiple Euler-Lagrange system consensus with the proposed filter can be guaranteed to be similar to that with the traditional controller when the communication network is not injected into false data by a malicious attacker. Furthermore, the consensus criteria of a multiple Euler-Lagrange system and the parameter design scheme of the proposed adaptive filter are achieved by using Lyapunov stability theory. Finally, a numerical example is carried out to demonstrate the effectiveness of the proposed consensus controller for a multi-Euler-Lagrange system attacked by using the false data injection.INDEX TERMS Euler-Lagrange systems, leader-following consensus, deception attack, distributed filter.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leader-Following Consensus of Multiple Euler-Lagrange Systems Under Deception Attacks

et al. 2021

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“…erefore, related research on discrete systems is necessary. Many scholars have begun to study the issues related to discrete-time MASs [32][33][34][35][36]. In [34], a discrete form of ILC algorithm was designed for the discrete MASs consensus tracking problem with random noise.…”

Section: Introductionmentioning

confidence: 99%

“…In [35], the consensus tracking problem of a class of nonlinear discrete MASs is studied when the iteration length changes randomly. In [36], with the help of ILC algorithm, the consensus tracking problem of discrete-time MASs with cooperative competition network is solved.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Wang

Dai

et al. 2021

Complexity

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This paper considers the consensus control problem of nonlinear spatial-temporal hyperbolic partial difference multiagent systems and parabolic partial difference multiagent systems with time delay. Based on the system’s own fixed topology and the method of generating the desired trajectory by introducing virtual leader, using the consensus tracking error between the agent and the virtual leader agent and neighbor agents in the last iteration, an iterative learning algorithm is proposed. The sufficient condition for the system consensus error to converge along the iterative axis is given. When the iterative learning number k approaches infinity, the consensus error in the sense of the L 2 norm between all agents in the system will converge to zero. Furthermore, simulation results illustrate the effectiveness of the algorithm.

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Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Zhao

2021

Intl J Robust & Nonlinear

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This article proposes a distributed model‐free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete‐time multi‐agent systems (MASs) with sensor saturation and measurement disturbance to perform consensus tracking tasks. Meanwhile, both fixed and switching topologies are considered, where only a subset of agents can acquire the desired trajectory information in each topology. A time‐varying linear data model for each agent is first established by utilizing the dynamic linearization method to formulate this algorithm. Merely, the input data and the saturated output data with measurement disturbance of each agent are applied to construct the DMFACTC algorithm without employing any dynamics model information of MASs. The convergence of the designed scheme is strictly proved. It illustrates that the output saturation and switching topologies do not affect the stability of MASs. Moreover, even if the sensor saturation, measurement disturbance, and switching topologies happen simultaneously, the DMFACTC also guarantees that the tracking errors of MASs converge to a small range around the origin. Furthermore, two numerical simulations and a realistic filling system simulation further verify the correctness and effectiveness of the theoretical results.

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Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

Cited by 10 publications

References 44 publications

Leader-Following Consensus of Multiple Euler-Lagrange Systems Under Deception Attacks

Leader-Following Consensus of Multiple Euler-Lagrange Systems Under Deception Attacks

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

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