Konferenční příspěvek

Král L., Hering P., Šimandl M.

Proceedings of the 15th IFAC Symposium on System Identification

Saint-Malo, France

2009

1505-1510

prof. Ing. Miroslav Šimandl, CSc. (FAV Západočeská univerzita, katedra kybernetiky)

neural networks, adaptive control, stochastic control

clanek_SYSID09_LK.pdf [file]

This paper presents an enhancement of a functional adaptive control of nonlinear stochastic systems that renders it to be robust with respect to the occurrence of outliers in the plant measured output. Outliers are considered to be large deviations of a signal being measured, only occurring in a few percent of the observations. Therefore, although rare, the outliers cause poor parameter estimates and, consequently, heavily degrade control performance due to their large amplitude. A system is modelled using a multi-layer perceptron network and the measurement noise is modelled by a mixture of Gaussian distributions. One component of the mixture describes uncorrupted process data, while the others describe various types of outliers. Parameters of the network together with output prediction of the uncorrupted data component are estimated by an estimation method based on the mixture of Gaussian distributions. Control design is based on a bicriterial dual approach. The advantages of the proposed controller are illustrated in an example by simulation and Monte Carlo analysis.