Evolutionary-programming-based tracker for hybrid chaotic interval systems

Jason S.H. Tsai, Ken M. Chen, Jennifer M. Madsen, Leang S. Shieh, Shu M. Guo

Research output: Contribution to journalArticlepeer-review


The nominal optimal tracker for the chaotic, nonlinear, interval system is first proposed in this paper. Initially 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 will 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 case in which the system has unknown-but-bounded interval parameters. The proposed evolutionary programming (EP) technique yields the strongest species to survive, reproduce themselves, and create more outstanding offspring. The worst-case realization of the sampled-data, nonlinear, uncertain system represented by the interval form with respect to the implemented 'best' tracker is also found in this paper for demonstrating the effectiveness of the proposed tracker.

Original languageEnglish
Pages (from-to)285-309
Number of pages25
JournalIMA Journal of Mathematical Control and Information
Issue number3
Publication statusPublished - 2005 Sep

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization
  • Applied Mathematics


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