Identification of linear parameter varying system with dual-rate sampled data and uncertain measurement delay

The parameter estimation for linear parameter varying (LPV) system with dual-rate sampled data in presence of unknown measurement delays is considered in this paper. The local identification approach is adopted and the global LPV model is constructed by synthesizing several local models by using the probability functions. The identification problem is formulated in the scheme of expectation-maximization (EM) algorithm and dual-rate sampled data, random measurement delays, and parameter varying property of the system are handled simultaneously. The iterative formulas to estimate the model parameters, measurement delays, and unknown parameters in probability functions based on dual-rate sampled data are derived. One simulation example is employed to demonstrate the effectiveness of the proposed method.

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