Shen, Dong; Zhang, Chao; Xu, Jian-Xin Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems. (English) Zbl 1426.93306 Int. J. Robust Nonlinear Control 29, No. 13, 4328-4347 (2019). 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. Cited in 7 Documents MSC: 93D99 Stability of control systems 93A16 Multi-agent systems 93B70 Networked control 93C10 Nonlinear systems in control theory Keywords:composite energy function; distributed iterative learning control; multiagent systems; neural networks; norm-bounded uncertainty PDFBibTeX XMLCite \textit{D. Shen} et al., Int. J. Robust Nonlinear Control 29, No. 13, 4328--4347 (2019; Zbl 1426.93306) Full Text: DOI