NDLI: Identification and control for singularly perturbed systems using multi-time-scale neural networks

Content Provider	IEEE Xplore Digital Library
Author	Dongdong Zheng Wenfang Xie Xuemei Ren
Copyright Year	2015
Description	Author affiliation: Dept. of Mech. & Ind. Eng., Concordia Univ., Montreal, QC, Canada (Dongdong Zheng; Wenfang Xie) \|\| Sch. of Autom., Beijing Inst. of Technol., Beijing, China (Xuemei Ren)
Abstract	Many well established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In this paper, a new adaptive identification method for singularly perturbed nonlinear system using multi-time-scale recurrent high-order neural networks is proposed to obtain an accurate and faithful model. By extending the usage of the optimal bounded ellipsoid concept, which is originally designed for discrete time systems, a novel weight updating law is developed for tuning the weights of the continuous time neural networks during the identification process. Based on the identification results, an indirect adaptive control scheme using singular perturbation theory is developed. By using singular perturbation theory, the system order is reduced, and the controller structure is simplified. The upper bound ε* for the small parameter ε is also obtained, such that for all 0 <; ε <; ε*, the estimated tracking errors will converge to 0 exponentially, and the tracking error will be bounded. The closed-loop stability is analyzed and the effectiveness of the identification and control scheme is demonstrated by simulation results.
Starting Page	1233
Ending Page	1239
File Size	703989
Page Count	7
File Format	PDF
e-ISBN	9781467391047
DOI	10.1109/ICInfA.2015.7279475
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Adaptation models optimal bounded ellipsoid singularly perturbed system Artificial neural networks Recurrent high-order neural network multi-time-scale system Nonlinear systems Adaptive control Ellipsoids
Content Type	Text
Resource Type	Article

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