Adaptive Decentralized NN Control of Nonlinear Interconnected Time-Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining LyapunovKrasovskii functions and the dynamic surface control (DSC) technique, along with the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the LyapunovKrasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of computational explosion, dimension curse, and controller singularity. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.