Evolutionary-programming-based tracker for hybrid chaotic interval systems
| Autor: | Shu-Mei Guo, Jennifer M. Madsen, Leang-San Shieh, Ken M. Chen, Jason Sheng Hong Tsai |
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| Rok vydání: | 2005 |
| Předmět: | |
| Zdroj: | IMA Journal of Mathematical Control and Information. 22:285-309 |
| ISSN: | 1471-6887 0265-0754 |
| DOI: | 10.1093/imamci/dni028 |
| Popis: | The nominal optimal tracker for the chaotic nonlinear interval system is first proposed in this thesis. First, we use an optimal linearization methodology to obtain the exact linear models of a class of discrete-time nonlinear time-invariant systems at operating states of interest, so that the conventional tracker can work for the nonlinear systems. A prediction-based digital tracker using the state-matching digital redesign method from a predesigned, state-feedback, continuous-time tracker for a hybrid chaotic system is presented. Then, we discuss the system has interval parameters. The interval system treated has interval and bounded parameters. The proposed evolutionary programming (EP) technique yields the strongest species to survive, reproduces themselves, and creates more outstanding offspring. The worst-case realization of the sampled-data nonlinear uncertain systems represented by the interval form with respect to the implemented "best" tracker is also found in this thesis for demonstrating the effectiveness of the proposed tracker. *The student **The advisor |
| Databáze: | OpenAIRE |
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