High‐precision formation control of nonlinear multi‐agent systems with switching topologies: A learning approach |
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| Authors: | Deyuan Meng Yingmin Jia Junping Du Jun Zhang |
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| Affiliation: | 1. The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China;2. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;3. School of Electronic and Information Engineering, Beihang University (BUAA), Beijing 100191, China |
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| Abstract: | Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi‐agent systems under directed networks with switching topologies. The agents have discrete‐time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi‐agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi‐agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | high‐precision formation nonlinear multi‐agent systems switching topologies iterative learning distributed algorithms directed networks |
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