Mats Ekman

IDENTIFICATION OF LINEAR SYSTEMS WITH ERRORS IN VARIABLES USING SEPARABLE NONLINEAR LEAST-SQUARES

Identification of linear systems with errors in variables using separable nonlinear least squares

A SEPARABLE NONLINEAR LEAST-SQUARES APPROACH FOR IDENTIFICATION OF LINEAR SYSTEMS WITH ERRORS IN VARIABLES

A separable nonlinear least-squares approach for identification of linear systems with errors in variables

Suboptimal control for the bilinear quadratic regulator problem: application to the activated sludge process

A suboptimal solution to the bilinear quadratic regulator problem with infinite final time is studied. The proposed control law is based on the steady-state solution of the state-dependent Riccati equation. A power-series approach of the Riccati equation is presented which results in offline calculations of one Riccati equation and a sequence of Lyapunov equations. Special emphasize is devoted to illustrate the performance of the derived control law applied to the activated sludge process.

A JAVA based simulator of activated sludge processes

A simulator of activated sludge processes in wastewater treatment plants has been implemented in the programming language JAVA. The simulator can be reached over Internet and operated from a web browser. A novel graphical user interface is used to operate the simulator and to present the simulation results in real time. Different automatic control strategies are implemented in order to illustrate the importance of automatic control in wastewater treatment plants. The simulator has been used for educational purposes both for university students and personnel from wastewater treatment plants. A demo version of the simulator is located at the URL address: www.syscon.uu.se/JASS/.

Parameter estimation of continuous-time bilinear systems based on numerical integration and separable non-linear least-squares

This paper considers the problem of estimating the parameters in continuous-time bilinear systems. The system identification approach is based on numerical integration and separable non-linear least-squares. The situation where the output signal is contaminated with noise is also discussed. The suggested estimation method uses a bias compensating approach and the model parameters together with parameters associated with the noise are estimated by solving an overdetermined system of equation in a least-squares sense.