The paper is focused on an implementation of a predictive controller with a colouring filter C in a disturbance model. The filter is often essential for practical applications of predictive control based on transfer function models. It is commonly considered as a design parameter because it has direct effects on closed loop performance. In this paper a computation of predictions for the case wi...

In this paper, the problem of identifying stochastic linear continuous-time systems from noisy input/output data is addressed. The input of the system is assumed to have a skewed probability density function, whereas the noises contaminating the data are assumed to be symmetrically distributed. The third-order cumulants of the input/output data are then (asymptotically) insensitive to the noise...

Several estimation methods have been proposed for identifying errors-invariables systems, where both input and output measurements are corrupted by noise. One of the promising approaches is the so called Frisch scheme. In its standard form it is designed to handle white measurement noise on the input and output sides. As the output noise comprises both effects of measurement errors and of proce...

Data envelopment analysis (DEA) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (DMUs) where multiple inputs and outputs exist. In the conventional applications of DEA, the data are considered as specific numerical values with explicit designation of being an input or output. However, the observed values of the data are sometimes imprec...

One of the problems associated with iterative learning control algorithms is the selection of a “proper” learning gain matrix for every discrete-time sample and for all successive iterations. This problem becomes more difficult in the presence of random disturbances such as measurement noise, reinitialization errors, and state disturbance. In this paper, the learning gain, for a selected learni...

In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical...

The paper considers the task of identifying a causal linear dynamic system excited by stationary gaussian zero mean noise of unknown spectrum, and given measurements of the system input and output contaminated by independent additive stationary noise 91g0813 of unknown spectra. While the solution is normally not unique, finite-dimensional parameterizations of the solution set are given (in some...