Tracking Performances of A Hot Air Blower System Using Different Types Of Controllers

System modeling is an important task to develop a mathematical model that describes the dynamics of a system. The scope for this work consists of modeling and controller design for a particular system. A heating and ventilation model is the system to be modeled and will be perturbed by pseudo random binary sequences (PRBS) signal. Parametric approach using AutoRegressive with Exogenous input (ARX) model structure will be used to estimate the mathematical model or approximated model plant. In this work, the approximated plant model is estimated using System Identification approach. Once the mathematical model is obtained, several controllers such as Self-Tuning Pole Assignment controller, Proportional-Integral- Derivative (PID) controller, and Generalized Minimum Variance (GMV) controller are designed and simulated in MATLAB. Finally, a comparative study based on simulation is analyzed and discussed in order to identify which controller deliver better performance in terms of the system’s tracking performances. It is found from a simulation done that a Self-Tuning Pole Assignment Servo-Regulator controller with a small value of pole give a best performance in term of its ability to eliminate error (%) and produce zero percentage of overshoot (%), while GMV controller using PSO tuning method offers a fast rise-time (), settling time (), and also its ability in eliminating (%)