Abstract
We present some generalisations of the classical (ordinary) Poisson moment functionals (PMF) approach towards improving the quality of estimation in continuous-time models of lumped linear single input single output(SISO) dynamical systems. Some results of an investigation on the influence of Poisson filter constant on the quality of estimation and useful guidelines for the proper choice of filter constant are discussed. This generalised PMF approach has been used for combined parameter and state estimation in a linear tuneinvariant system based on recursive least squares algorithm. This algorithm can be extended for identification of time-varying systems. A recursive instrumental variable (IV) algorithm based on PMFs of the process data is developed to reduce the bias in the estimation. Finally, an attempt has been made to apply the algorithm for the identification of parameters of a system operating under closed loop.
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© 1991 Springer Science+Business Media Dordrecht
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Saha, D.C., Bapat, V.N., Roy, B.K. (1991). The Poisson moment functional technique — Some new results. In: Sinha, N.K., Rao, G.P. (eds) Identification of Continuous-Time Systems. International Series on Microprocessor-Based Systems Engineering, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3558-0_11
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DOI: https://doi.org/10.1007/978-94-011-3558-0_11
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