Abstract
This paper proposes Extended Least Squares (ELS) schemes for ARMAX model identification of continuous-time systems. The schemes have a relaxed Strictly Positive Real (SPR) condition for global convergence. The relaxed SPR scheme is achieved by introducing overparametrisation and prefiltering but without introducing ill-conditioning. The schemes presented are the first such proposed for continuous-time systems. The concepts developed here carry through to output-error, fast-sampled continuous-time systems and associated discrete-time ELS algorithms. We also state conditions for the persistence of excitation (P.E.) of the regression vectors in the proposed ELS schemes to assure strong consistency and obtain convergence rates.
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