Performance Analysis of First-Order Plus Dead-Time Processes Using Generalized Predictive Control

Abstract

Generalized Predictive Control (GPC) is a part of a family of Model Predictive Controllers, which predicts the future output using the concept of Receding Horizon. A cost function is then optimized based on the predicted error and optimized control sequence is calculated. The standard GPC algorithm, proposed in 1987, finds many applications. It has several modifications and extensions to incorporate adaptiveness and constraints. Though GPC is inherently discrete, current research trend is development of continuous GPC algorithms. GPC controller has four tuning parameters, control weight, minimum and maximum prediction horizon and control horizon. This thesis tries to address the effect of these tuning parameters on the performance of a First-Order Plus Dead-Time (FOPDT) process. The performance was measured in terms of three important dynamic characteristics- settling time, rise time and peak overshoot. Lower values of control weight and higher values of prediction horizon gave better performance with regards to these criteria. The observations from these simulations were used as guidelines for tuning the GPC controller for a tank level control system. Single tank system is an example of an FOPDT process. Due to the non-linearity in the process model, it was found to perform better under adaptive GPC (AGPC) algorithm. Simulations were also performed to take the input constraints into consideration. Then, disturbance rejection and robustness behaviour of the adaptive constrained GPC (ACGPC) controller was studied. It was found that, GPC gives many configurable parameters to deal with different control problems. The performance was found to be better than conventional PI controller.