In the paper a new approach to fuzzy confidence interval identification is presented. The method combines a fuzzy identification methodology with some ideas from statistics. The idea is to find, on a finite set of measured data, the confidence interval defined by the lower and upper fuzzy bound that define the band that contains all the output measurements. The fuzzy confidence interval model can be used in process monitoring, fault detection or in the case of robust control design.
COBISS.SI-ID: 6931284
This paper deals with a novel formulation of continuous-time model-predictive control for nonlinear systems. A nonlinear-mapping approximation, employing a PWL approximation, is also an integral part of the control scheme, and thus removes the need for output-function invertibility. The analytical formulation of the control law makes it possible to use the method in practice, especially in the chemical industry. An illustrative experiment is conducted to compare the proposed approach with the method of nonlinear H1 control of a pH-neutralization process.
COBISS.SI-ID: 7440468