Consensus control for multi-agent systems with quasi-one-sided Lipschitz nonlinear dynamics via iterative learning algorithm

Fu, Qing; Li, Xiangdong; Du, Lizhi; Xu, Guangzhao; Wu, Jianrong

doi:10.1007/s11071-017-4035-7

Cited by 27 publications

(14 citation statements)

References 26 publications

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“…In light of the advantages of iterative learning controls, many scholars have applied iterative learning controls into the coordination controls of multi-agent systems [24,27,[33][34][35][36][37][38][39]. In [27], the distributed iterative learning control method was designed to solve the formation control problem of discrete-time multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

“…In [27], the distributed iterative learning control method was designed to solve the formation control problem of discrete-time multi-agent systems. For a class of non-affine nonlinear multi-agent systems, a kind of iterative learning control algorithm was proposed in [33], if the number of iterations was sufficiently large, so that the agent outputs can reach consensus. For discrete-time linear multiagent systems with communication delays and switching topologies, a kind of iterative learning control protocol was also devised in [34], obtaining consensus for multi-agent outputs in a finite time interval.…”

Section: Introductionmentioning

confidence: 99%

“…All the abovementioned results set the initial state of the system based on two assumptions in order to guarantee the convergence of the algorithms. The first assumes that the initial state of the system is exactly the same as the expected state at each iteration [24,36]; the other assumes that the initial state stays fixed at each iteration [27,[33][34][35][37][38]. The two assumptions are too strict for multi-agent systems in practical operations.…”

Section: Introductionmentioning

confidence: 99%

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Consensus Tracking via Iterative Learning for Multi-Agent Systems With Random Initial States

2020

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In this paper, a distributed consensus iterative learning control algorithm is proposed for the finite-time consensus tracking of multi-agent systems with random initial states. The tracking errors from the agent itself and its neighbours are applied to successively rectify the control protocol when only some agents can obtain the desired trajectory information. Additionally, a time interval is designed in the control protocol, and the random initial state errors are rectified, one-by-one, in the time interval. The interval can gradually shorten as the number of iterations increases. Furthermore, the convergence of the algorithm is theoretically proven by the contraction mapping method, and the convergence condition is derived. The proposed algorithm can cause the time interval to gradually shorten with the iterations increase, during the time, the desired trajectory cannot be consistently tracked due to the random initial state errors. As a consequence, the algorithm gradually widens the time interval for full consensus tracking of the multi agents. Finally, the simulation examples are provided to verify the effectiveness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Consensus Tracking via Iterative Learning for Multi-Agent Systems With Random Initial States

2020

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“…The basic idea of ILC is to improve the current tracking performance by making use of the past control experience. On account of its simplicity and effectiveness, ILC has attracted tremendous attention in the field of control theory, and lots of efforts have been devoted to the progress of ILC in recent years (see other works and references therein). Meanwhile, ILC has been widely applied in many practical systems with the properties of repetitive, such as robotic manipulators, hard disk drives, and transportation systems (see the works of Sun et al, Chen et al, and Hou et al).…”

Section: Introductionmentioning

confidence: 99%

D‐type iterative learning control for one‐sided Lipschitz nonlinear systems

Tian

2019

Intl J Robust & Nonlinear

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Summary In this paper, the problem of iterative learning control for a class of nonlinear systems is studied. Here, the nonlinear functions satisfy the one‐sided Lipschitz and quadratically inner‐bounded conditions. For such nonlinear systems, open‐loop and closed‐loop D‐type learning algorithms are adopted, respectively, and furthermore, the convergence conditions of the D‐type learning algorithms are established. It is shown that both algorithms can ensure that the system output converges to the desired trajectory on the whole time interval. Finally, the validity of the presented D‐type learning algorithms is verified by a numerical example.

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“…e consensus problem, as an interesting topic for MASs, has achieved many results [5][6][7][8][9][10]. However, most studies have focused on the consensus of integer-order MASs (IOMASs).…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking of Fractional‐Order Multiagent Systems via Fractional‐Order Iterative Learning Control

Pan

et al. 2019

Complexity

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In this work, the consensus problem of fractional-order multiagent systems with the general linear model of fixed topology is studied. Both distributed PDα-type and Dα-type fractional-order iterative learning control (FOILC) algorithms are proposed. Here, a virtual leader is introduced to generate the desired trajectory, fixed communication topology is considered, and only a subset of followers can access the desired trajectory. The convergence conditions are proved using graph theory, fractional calculus, and λ norm theory. The theoretical analysis shows that the output of each agent completely tracks the expected trajectory in a limited time as the iteration number increases for both PDα-type and Dα-type FOILC algorithms. Extensive numerical simulations are given to demonstrate the feasibility and effectiveness.

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Consensus control for multi-agent systems with quasi-one-sided Lipschitz nonlinear dynamics via iterative learning algorithm

Cited by 27 publications

References 26 publications

Consensus Tracking via Iterative Learning for Multi-Agent Systems With Random Initial States

Consensus Tracking via Iterative Learning for Multi-Agent Systems With Random Initial States

D‐type iterative learning control for one‐sided Lipschitz nonlinear systems

Consensus Tracking of Fractional‐Order Multiagent Systems via Fractional‐Order Iterative Learning Control

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