Iterative Learning Control for MIMO Singular Distributed Parameter Systems

Dai, Xisheng; Zhou, Xingyu; Tian, Senping; Ye, Hongtao

doi:10.1109/access.2017.2768480

Cited by 28 publications

(16 citation statements)

References 28 publications

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“…Consider effective repeated operation techniques, iterative learning control has been proved for its ability to achieve a perfect tracking performance by utilizing past control experience. The ILC is certified not only efficient to a single system [16]- [19] but also applicable to multiagent systems. In [20], a distributed ILC is considered for multi-agent tracking control and multi-agent formation control respectively.…”

Section: A Related Workmentioning

confidence: 99%

Iterative Learning Consensus Tracking Control for Nonlinear Multi-Agent Systems With Randomly Varying Iteration Lengths

Liang

Wang

et al. 2019

IEEE Access

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This paper is mainly devoted to a distributed iterative learning control design for a class of nonlinear discrete-time multi-agent systems in the presence of randomly varying iteration lengths. A stochastic variable is introduced and utilized to construct a consensus error with iteration-varying lengths. The distributed ILC law using the consensus error term is considered, contraction mapping and λ-norm technique methods are employed to develop a sufficient condition for the asymptotic stability of ILC. It is shown that all agents can be guaranteed to achieve finite-time tracking with randomly varying iteration lengths, even under the condition that the desired trajectory is available to not all, but only a portion of agents. The proposed algorithm is also extended to achieve consensus control for switching topologies multi-agent systems with iteration-varying lengths. Two illustrative examples are given to demonstrate the effectiveness of the theoretical results. INDEX TERMS Multi-agent systems (MAS), consensus tracking, randomly length varying, iterative learning control (ILC), convergence.

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Section: A Related Workmentioning

confidence: 99%

Iterative Learning Consensus Tracking Control for Nonlinear Multi-Agent Systems With Randomly Varying Iteration Lengths

Liang

Wang

et al. 2019

IEEE Access

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“…Iterative learning control (ILC) has been widely utilized to cope with the repeated tracking control with high precision requirement in the fixed time interval due to its simplicity and effectiveness [8], [9]. Hence, ILC has been successfully implemented to many kinds of multi-agent systems in recent references, such as high-order nonlinear MASs [10], singular MASs [11], fractional-order MASs [12], and distributed parameter MASs [13]- [15], etc..…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Wang

Zhou

et al. 2020

IEEE Access

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This paper investigates the leader-following consensus tracking problems via iterative learning control for singular fraction-order multi-agent systems in the presence of iteration-varying switching topologies and initial state errors. First, in order to eliminate the impulsive effect of singular systems and handle iteration-varying topologies, the closed-loop D α-type iterative learning control protocol is proposed. To deal with initial state errors, the initial state learning laws are introduced in light of the initial output errors of each follower agent. The developed D α-type learning protocols based on initial state learning laws can guarantee each follower track perfectly the leader agent in the fixed time interval. Next, the sufficient convergent conditions of consensus tracking errors are provided. Moreover, the D α-type learning protocols are extended to nonlinear singular fraction-order multi-agent systems with iteration-varying topologies and initial state errors. Finally, two numerical examples are presented to verify the validity of the proposed D α-type learning scheme in this paper.

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“…An observer‐based ILC scheme, where the observer has an iterative form, is utilised to handle the trajectory tracking problem of a rigid robot manipulator [34]. In [35], ILC is extended to PDE MIMO singular distributed parameter systems, where a proportion‐type ILC controller is used to track the desired goal. Despite various ILC schemes have been suggested in [33–35], most of them are complex to implement, which need extra sensors in certain cases.…”

Section: Introductionmentioning

confidence: 99%

“…In [35], ILC is extended to PDE MIMO singular distributed parameter systems, where a proportion‐type ILC controller is used to track the desired goal. Despite various ILC schemes have been suggested in [33–35], most of them are complex to implement, which need extra sensors in certain cases. In [36], proportion type ILC scheme combined with tangent function is proposed for a series of typical flexible structures with disturbances and input constraints.…”

Section: Introductionmentioning

confidence: 99%

Vibration suppression of an Euler–Bernoulli beam by backstepping iterative learning control

Liu

Zhan

Gao

et al. 2019

IET Control Theory & Applications

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Iterative Learning Control for MIMO Singular Distributed Parameter Systems

Cited by 28 publications

References 28 publications

Iterative Learning Consensus Tracking Control for Nonlinear Multi-Agent Systems With Randomly Varying Iteration Lengths

Iterative Learning Consensus Tracking Control for Nonlinear Multi-Agent Systems With Randomly Varying Iteration Lengths

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Vibration suppression of an Euler–Bernoulli beam by backstepping iterative learning control

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