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The scope of this research is the identification of unknown piecewiseconstant parameters of linear regression equation under the finite excitationcondition. Compared to the known methods, to make the computational burdenlower, only one model to identify all switching states of the regression isused in the developed procedure with the following two-fold contribution. Firstof all, we propose a new truly online estimation algorithm based on awell-known DREM approach to detect switching time and preserve time alertnesswith adjustable detection delay. Secondly, despite the fact that a switchingsignal function is unknown, the adaptive law is derived that provides globalexponential convergence of the regression parameters to their true values incase the regressor is finitely exciting somewhere inside the time intervalbetween two consecutive parameters switches. The robustness of the proposedidentification procedure to the influence of external disturbances isanalytically proved. Its effectiveness is demonstrated via numericalexperiments, in which both abstract regressions and a second-order plant modelare used.

Unknown Piecewise Constant Parameters Identification with Exponential Rate of Convergence