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Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems. (English) Zbl 1426.93306

Summary: This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system’s unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.

MSC:

93D99 Stability of control systems
93A16 Multi-agent systems
93B70 Networked control
93C10 Nonlinear systems in control theory
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