Reach control problem for affine multi-agent systems on simplices

In this paper, the leader-following [Formula: see text] consensus problem for discrete-time nonlinear multi-agent systems with delay and parameter uncertainty is investigated, with the objective of designing an output feedback protocol such that the multi-agent system achieves leader-following consensus and has a prescribed [Formula: see text] performance level. By model transforming, the leader-following consensus control problem is converted into robust [Formula: see text] control problem. Based on the Lyapunov function technology and the linear matrix inequality method, some new sufficient conditions are derived to guarantee the consensus of discrete-time nonlinear multi-agent systems. The feedback gain matrix and the optimal [Formula: see text] performance index are obtained in terms of linear matrix inequalities. Finally, numerical examples are provided to illustrate the effectiveness of the theoretical results.

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Tracking control problem in general linear and Lipschitz nonlinear multi-agent systems with jointly connected topology

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2020.04.006 ◽

2020 ◽

Vol 357 (10) ◽

pp. 6121-6136

Author(s):

Ahmadreza Jenabzadeh ◽

Weidong Zhang

Keyword(s):

Control Problem ◽

Tracking Control ◽

General Linear ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent

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Iterative learning control for nonlinear heterogeneous multi-agent systems with multiple leaders

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220941636 ◽

2020 ◽

pp. 014233122094163

Author(s):

Qin Fu

Keyword(s):

Control Problem ◽

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

Time Interval ◽

Multi Agent Systems ◽

Containment Control ◽

Agent Systems ◽

Multi Agent ◽

Multiple Leaders

This article investigates the iterative learning control problem for a class of nonlinear heterogeneous multi-agent systems. The main contribution of this article is to apply iterative learning control algorithm to the multi-agent systems with multiple leaders, and solve the containment control problem of multi-agent systems in the sense of iterative learning control stability. Based on the framework of communication topologies, distributed iterative learning controllers are designed. And when the iterative learning control laws are applied to the systems, the containment errors between the followers’ states and the convex hull spanned by the leaders’ states over a finite-time interval are bounded, and furthermore, the containment errors can converge to zero as the iteration index approaches to infinity in the absence of initial errors. A simulation example is finally constructed to verify the effectiveness of the theoretical method.

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A General Solution to the Formation Control Problem Without Collisions for First-Order Multi-Agent Systems

Robotica ◽

10.1017/s0263574719001280 ◽

2019 ◽

Vol 38 (6) ◽

pp. 1123-1137

Author(s):

J. F. Flores-Resendiz ◽

E. Aranda-Bricaire

Keyword(s):

General Solution ◽

Control Problem ◽

Formation Control ◽

Vector Fields ◽

Multi Agent Systems ◽

Agent Systems ◽

First Order ◽

Communication Graphs ◽

Cyclic Pursuit ◽

Multi Agent

SUMMARYIn this paper, a general solution to the formation control problem without collisions for first-order multi-agent systems is proposed. The case of an arbitrary number of mobile agents on a plane with saturated input velocity is analysed. Besides, conditions on the communication graph among agents are relaxed to the only requirement of containing a directed spanning tree. This general approach is an extended result from the simpler case of combinations of cyclic pursuit communication graphs. The proposed solution to this problem is designed in two steps. First, the asymptotic convergence in the absence of collisions is ensured. After this, the non-collision problem is faced by analysing the most general possible geometrical scenario which can lead to collision among agents. Discontinuous vector fields with unstable counterclockwise focus behaviour are applied by every agent in order to repel each other. Numerical simulations illustrate the performance of the proposed scheme.

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