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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 International Journal of Control Automation and Systems International Journal of Control Automation and System Vol.6 No.1
- 발행연도
- 2008.2
- 수록면
- 24 - 34 (11page)
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초록· 키워드
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A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.
목차
Abstract
1. INTRODUCTION
2. STANDARD NEURAL NETWORK MODEL
3. FEEDBACK STABILIZATION OF THE SNNM
4. APPLICATION EXAMPLES
5. CONCLUSIONS
REFERENCES
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