Digital Self-tuning Control for Pressure Process

Abstract

Two digital control systems - Self-tuning PID (Proportional-Integral-Derivative) Control and Predictor-based self-tuning control with constraints - for the continuous-time pressure process control are presented in this paper. The digital self-tuning PID control with optimization of closed-loop parameters and sampling period is proposed. The multidimensional optimization problem of closed-loop parameters and sampling period is solved by subcomponent search method that enables dividing the problem to one-dimensional optimization problems. The golden section search is adjusted to solve those - one-dimensional - optimization problems. The predictor-based self-tuning control with constraints is adapted for both minimum-phase and nonminimum-phase process models. The control quality of pressure process of both control systems has been experimentally investigated. The results of experimental analysis demonstrate that the digital self-tuning PID control with optimization is more efficient as compared to predictive-based self-tuning control with constraints for pressure process.