Name
Title | ||
|---|---|---|
Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation |
Abstract | ||
|---|---|---|
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. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov-Krasovskii 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 constrains are conducted with the help of an auxiliary design system based on the Lyapunov-Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of ''computational explosion'', ''dimension curse'' and ''controller singularity'' are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neucom.2011.03.005 | Neurocomputing |
Keywords | Field | DocType |
decentralized adaptive nn tracking,controller singularity,input saturation,time-delay systems,neural networks,minimal-learning parameters (mlp),control scheme,decentralized adaptive control,proposed controller,unknown time delay,decentralized adaptive neural control,novel decentralized adaptive neural,adaptive parameter,dynamic surface control,dynamic surface control (dsc),proposed scheme,lyapunov–krasovskii functions,proposed control scheme,large-scale system,adaptive control,numerical simulation,neural network,stability analysis | Neural control,Control theory,Nonlinear system,Saturation (chemistry),Computer simulation,Control theory,Singularity,Design systems,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
74 | 14-15 | Neurocomputing |
Citations | PageRank | References |
43 | 1.63 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tieshan Li | 1 | 1723 | 81.13 |
| Ronghui Li | 2 | 74 | 3.36 |
| Junfang Li | 3 | 46 | 2.40 |