Title: Design of state and parametric estimation algorithms for nonlinear systems with hysteresis-saturation nonlinearities
Authors: Houda Salhi; Samira Kamoun
Addresses: Laboratory of Sciences and Technique of Automatic Control and Computer Engineering (Lab-SAT), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia ' Laboratory of Sciences and Technique of Automatic Control and Computer Engineering (Lab-SAT), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia
Abstract: The paper deals with the parameter estimation problem of Wiener state-space models with hysteresis-saturation nonlinearities. A recursive parametric and state estimation algorithm is presented for the Wiener system by combining the adjustable model idea, the least squares technique and the Kalman filter principle. The basic idea is to decompose the hysteresis-saturation nonlinearity into two asymmetric saturation nonlinearities and to estimate jointly the state variables, the parameters and the internal variable of the considered Wiener model using the available input-output data. The proposed recursive algorithm can be extended to nonlinear systems with other hard nonlinearities.
Keywords: recursive algorithm; parameter estimation; state estimation; Wiener state space models; hysteresis-saturation nonlinearity; adjustable model; least squares; Kalman filter; nonlinear systems; modelling.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.2, pp.146 - 153
Available online: 16 Mar 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article