Experiment design for parameter estimation in nonlinear systems based on multilevel excitation

An experiment design procedure for parameter estimation in nonlinear dynamical systems is presented in this paper. The input to the system is designed in such a way that the information content of the data, as measured by a scalar function of the information matrix, is maximized. By restricting the input to a finite number of possible levels, the experiment design problem is formulated as a convex optimization problem which can be solved efficiently. The method is applied to a Continuous Stirred Tank Reactor in a simulation study. The parameter estimation based on the input signal obtained in our procedure is shown to outperform the one based on random binary signals.