Worst-Case Identification Using Quantized Observations
In this chapter, the parameter identification problem under unknown-butbounded disturbances and quantized output sensors is discussed. In Chapter 9, an input sequence in (9.5) was used to generate observation equations in which only one parameter appears, reducing the problem to the identification of gain systems. A more general input design method is introduced in this chapter to achieve parameter decoupling that transforms a multiparameter model into a single-parameter model. The input sequence with the shortest length that accomplishes parameter decoupling is sought. Identification algorithms are introduced, and their convergence, convergence rates, and time complexity for achieving a predefined estimation accuracy are investigated.
KeywordsTime Complexity Input Sequence Observation Equation Target Output Optimal Input
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- M. Casini, A. Garulli, and A. Vicino, Time complexity and input design in worst-case identification using binary sensors, in Proc. 46th IEEE Conf. Decision Control, 5528–5533, 2007.Google Scholar