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

Dongdong Zheng, Wen-Fang Xie, Xuemei Ren

IEEE Transactions on Neural Networks and Learning Systems

|January 8, 2016

Summary

This study introduces a novel neural network (NN) identification scheme for singularly perturbed systems, improving model accuracy. The developed method offers faster convergence and simplifies control design, ensuring system stability and state convergence.

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Area of Science:

Control Systems Engineering
Computational Neuroscience
Nonlinear Dynamics

Background:

Singularly perturbed systems often require complete model parameter knowledge for established theories.
Existing identification schemes may lack accuracy or efficiency for complex nonlinear systems.

Purpose of the Study:

To propose a new identification scheme for singularly perturbed nonlinear systems using multitime-scale recurrent high-order neural networks (NNs).
To develop an efficient control scheme based on the accurate system identification.
To ensure closed-loop stability and convergence of system states.

Main Methods:

A novel weight updating law for continuous-time NNs inspired by the optimal bounded ellipsoid algorithm.
Utilizing multitime-scale recurrent high-order neural networks for system identification.
Applying singular perturbation methods for system order reduction and controller simplification.

Main Results:

The proposed NN identification scheme achieves faster convergence compared to gradient-descent methods due to an adaptive learning rate.
The developed control scheme effectively reduces system order and simplifies controller structure.
Simulation results validate the effectiveness of both the identification and control strategies.

Conclusions:

The novel NN-based identification scheme provides an accurate and efficient method for singularly perturbed nonlinear systems.
Singular perturbation theory, combined with the new identification method, enables robust and simplified control system design.
The approach guarantees closed-loop stability and convergence, demonstrating practical applicability.