Abstract
Three direct methods for the identification of a linear continuous-time system from the samples of input and output observations are considered. These are based on obtaining approximate expressions for the signals from their samples. The methods are (i) block-pulse functions, (ii) trapezoidal pulse functions, and (iii) cubic spline functions. In each case, the differential equations are integrated using these approximations and the results are used for estimating the parameters of a model of given order and structure which will provide the best fit in the least squares sense. Results of simulation are included which compare the relative performance of the three methods both in the absence and the presence of measurement noise.
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