Predictor-based self-tuning control of pressure plants

Abstract

A digital predictor-based self-tuning control with constraints for the pressure plants, which is able to cope with minimum-phase and nonminimum-phase plant models is presented in this paper. Determined that applying polynomial factorization for such models the characteristic polynomials of closed-loops are changed. Therefore, the on-line identification of the models’ parameters is so performed that ensures stable closed-loops. A choice of the sampling period in digital control typically impacts a control quality of the plant, thus we propose a method for optimization of a sampling period in the digital predictor-based self-tuning control system. The impact of the selection of the sampling period and input signals’ constraints – amplitude boundaries and the change rate - to the control quality of the pressure plant was experimentally analysed.