Identification of stable models via nonparametric prediction error methods
This work addresses stability issues in system identification for control systems, but appears incremental as it builds on prior Bayesian approaches.
The paper tackles the problem of ensuring stability in linear system identification by proposing and comparing techniques to guarantee stable impulse responses, with simulation results provided.
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stable predictors based on the prediction error minimization. Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system. In this paper we propose and compare various techniques to address this issue. Simulations results comparing these techniques will be provided.