Informatica

The aim of the given paper is the development of an approach for parametric identification of Wiener systems with piecewise linear nonlinearities, i.e., when the linear part with unknown parameters is followed by a saturation-like function with unknown slopes. It is shown here that by a simple data reordering and by a following data partition the problem of identification of a nonlinear Wiener system could be reduced to a linear parametric estimation problem. Afterwards, estimates of the unknown parameters of linear regression models are calculated by processing respective particles of input-output data. A technique based on ordinary least squares (LS) is proposed here for the estimation of parameters of linear and nonlinear parts of the Wiener system, including the unknown threshold of piecewise nonlinearity, too. The results of numerical simulation and identification obtained by processing observations of input-output signals of a discrete-time Wiener system with a piecewise nonlinearity by computer are given.

The aim of the given paper is the development of an approach for parametric identification of Hammerstein systems with piecewise linear nonlinearities, i.e., when the saturation-like function with unknown slopes is followed by a linear part with unknown parameters. It is shown here that by a simple input data rearrangement and by a following data partition the problem of identification of a nonlinear Hammerstein system could be reduced to the linear parametric estimation problem. Afterwards, estimates of the unknown parameters of linear regression models are calculated by processing respective particles of input-output data. A technique based on ordinary least squares is proposed here for the estimation of parameters of linear and nonlinear parts of the Hammerstein system, including the unknown threshold of the piecewise nonlinearity, too. The results of numerical simulation and identification obtained by processing observations of input-output signals of a discrete-time Hammerstein system with a piecewise nonlinearity with positive slopes by computer are given.

Self-tuning control with recursive identification of extremal dynamic systems is considered. The systems can be represented by combinations of linear dynamic and extremal static parts, their output being disturbed by a coloured noise. Minimum-variance controllers for Hammerstein, Wiener, and Wiener-Hammerstein-type systems are designed taking into consideration restrictions for control signal magnitude and/or change rate. The estimates of unknown parameters in the controller equations are obtained in the identification process in the closed loop. The efficiency of self-tuning control algorithms is illustrated by statistical simulation. On the basis of worked out methods, adaptive systems for optimization of fuel combustion and steam condensation processes in thermal power units are developed.