摘要
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 micro-processors. Illustrative examples are presented to demonstrate the effectiveness of the proposed methodology.
原文 | English |
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頁(從 - 到) | 2433-2455 |
頁數 | 23 |
期刊 | International Journal of Bifurcation and Chaos in Applied Sciences and Engineering |
卷 | 15 |
發行號 | 8 |
DOIs | |
出版狀態 | Published - 2005 8月 |
All Science Journal Classification (ASJC) codes
- 建模與模擬
- 工程(雜項)
- 多學科
- 應用數學