Consensus tracking problem for linear fractional multi-agent systems with initial state error

In this paper, we discuss the consensus tracking problem by introducing two iterative learning control (ILC) protocols (namely, Dα-type and PDα-type) with initial state error for fractional-order homogenous and heterogenous multi-agent systems (MASs), respectively. The initial state of each agent is fixed at the same position away from the desired one for iterations. For both homogenous and heterogenous MASs, the Dα-type ILC rule is first designed and analyzed, and the asymptotical convergence property is carefully derived. Then, an additional P-type component is added to formulate a PDα-type ILC rule, which also guarantees the asymptotical consensus performance. Moreover, it turns out that the PDα-type ILC rule can further adjust the final performance. Two numerical examples are provided to verify the theoretical results.

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Distributed adaptive iterative learning control for the consensus tracking of heterogeneous nonlinear multi-agent systems

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220911833 ◽

2020 ◽

Vol 42 (13) ◽

pp. 2396-2409

Author(s):

Xiongfeng Deng ◽

Xiuxia Sun

Keyword(s):

Neural Network ◽

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

Tracking Problem ◽

Multi Agent Systems ◽

Adaptive Iterative Learning Control ◽

Agent Systems ◽

Consensus Tracking ◽

Multi Agent

This paper addresses the consensus tracking problem of leader-following heterogeneous multi-agent systems with iterative learning control. The model of heterogeneous multi-agent systems consists of first-order and second-order nonlinear dynamics. It is assumed that only a portion of following agents can receive the leader’s information. The radial basis function neural network is introduced to deal with the nonlinear terms of following agents. Then, the distributed adaptive iterative learning control protocols with neural network are designed for following agents with different dynamics. Meanwhile, the adaptive update control laws for the time-varying parameters are proposed. Theoretical analysis shows that the consensus tracking problem of the given multi-agent systems can be guaranteed on the time domain and iterative domain. Finally, the validity of theoretical results is verified by a simulation example.

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Iterative learning control for multi-agent systems with impulsive consensus tracking

Nonlinear Analysis Modelling and Control ◽

10.15388/namc.2021.26.20981 ◽

2021 ◽

Vol 26 (1) ◽

pp. 130-150

Author(s):

Xiaokai Cao ◽

Michal Fečkan ◽

Dong Shen ◽

JinRong Wang

Keyword(s):

Iterative Learning Control ◽

Multi Agent System ◽

Multi Agent Systems ◽

Initial State ◽

Impulsive Input ◽

Agent Systems ◽

Consensus Tracking ◽

Multi Agent ◽

Piecewise Continuous ◽

Virtual Leader

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory.

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