In:
International Journal of Bifurcation and Chaos, World Scientific Pub Co Pte Ltd, Vol. 15, No. 08 ( 2005-08), p. 2433-2455
Abstract:
This paper presents a neural network-based digital redesign approach for digital control of continuous-time chaotic systems with unknown structures and parameters. Important features of the method are that: (i) it generalizes the existing optimal linearization approach for the class of state-space models which are nonlinear in the state but linear in the input, to models which are nonlinear in both the state and the input; (ii) it develops a neural network-based universal optimal linear state-space model for unknown chaotic systems; (iii) it develops an anti-digital redesign approach for indirectly estimating an analog control law from a fast-rate digital control law without utilizing the analog models. The estimated analog control law is then converted to a slow-rate digital control law via the prediction-based digital redesign method; (iv) it develops a linear time-varying piecewise-constant low-gain tracker which can be implemented using microprocessors. Illustrative examples are presented to demonstrate the effectiveness of the proposed methodology.
Type of Medium:
Online Resource
ISSN:
0218-1274
,
1793-6551
DOI:
10.1142/S021812740501340X
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2005
SSG:
11
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