Identification and Control for Singularly Perturbed Systems Using Multitime-Scale Neural Networks

Dongdong Zheng, Wen Fang Xie, Xuemei Ren, Jing Na

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)

Abstract

Many well-established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In order to obtain an accurate and faithful model, a new identification scheme for singularly perturbed nonlinear system using multitime-scale recurrent high-order neural networks (NNs) is proposed in this paper. Inspired by the optimal bounded ellipsoid algorithm, which is originally designed for discrete-time systems, a novel weight updating law is developed for continuous-time NNs identification process. Compared with other widely used gradient-descent updating algorithms, this new method can achieve faster convergence, due to its adaptively adjusted learning rate. Based on the identification results, a control scheme using singular perturbation theories is developed. By using singular perturbation methods, the system order is reduced, and the controller structure is simplified. The closed-loop stability is analyzed and the convergence of system states is guaranteed. The effectiveness of the identification and the control scheme is demonstrated by simulation results.
Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date6 Jan 2016
DOIs
Publication statusE-pub ahead of print - 6 Jan 2016

Keywords

  • singularly perturbed system (SPS)., Feedback control, optimal bounded ellipsoid (OBE), recurrent high-order neural network (RHONN)

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