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A problem of identification of piecewise-constant unknown parameters of alinear regression equation (LRE) is considered. Such parameters change theirvalues over the interval of the regressor finite (rather than persistent)excitation. To solve it, the previously proposed I-DREM procedure and theintegral-based filtering method with the exponential forgetting and resettingare improved: the integral of the filter equations is taken over the finitetime intervals, which belong to the finite excitation time range. This allowsone to obtain an exponentially bounded identification error over the excitationinterval, and, when the LRE parameters are constant outside such interval, toprovide exponential convergence of the identification error to zero. Inaddition, the applied method of the regressor excitation normalization makes itpossible to obtain the same rate of convergence of the parameter error for theregressors of various amplitudes. The stability and all the above-mentionedproperties are proved for the derived identification method. The results ofnumerical experiments (including the case of the noise-contaminated regressorand output measurements) fully support the obtained theoretical results.

Exponential Convergence of Piecewise-Constant Parameters Identification under Finite Excitation Condition