Článek v odborném periodiku

Král L., Šimandl M.

International Journal of Adaptive Control and Signal Process

25

2011

11

949–964

1099-1115

Ing. Ladislav Král, Ph.D. (FAV Západočeská univerzita, katedra kybernetiky)

adaptive control;neural networks;nonlinear estimation;stocha

clanek_IJACS_KS.pdf [file]

The article deals with a challenging problem of adaptive control design for multivariable stochastic systems with a functional uncertainty. Model of the system is based on multi-layered perceptron neural networks where both the unknown parameters and the structure are found in real time without a necessity of any off-line training process. The unknown parameters are estimated by a global estimation method, the Gaussian sum filter, and the structure of the neural network model is optimized by a proposed pruning method. The control law is based on a bicriterial approach to the suboptimal dual control. Two individual criteria are designed and used to introduce conflicting efforts between the estimation and control; probing and caution. A comparison of the proposed dual control and its alternative with an implementation of the pruning algorithm is shown in a numerical example