Implementation of PRBS & RGS Perturbation Input Signals on Steam Temperature: Model Estimation and PID Control

Recent studies have conducted modelling using both linear and nonlinear approach to approximate the model of distillation column. This study presents the nonlinear modelling using Nonlinear Auto Regressive with Exogenous Input (NARX) model using Pseudorandom Binary Sequence (PRBS) and Random Gaussian Signal (RGS) perturbation input signals and PID control for distillation’s column temperature. System identification tools has been used for the NARX modelling estimation. The PID controllers with Ziegler Nichols (ZN) and Takahashi tuning methods were designed using MATLAB Simulink toolbox to regulate the steam temperature of Steam Distillation Pilot Plant (SDPP). The best model was evaluated based on high percentage of fitness and low value of mean square error (MSE) and final prediction error (FPE) while the selection of the best PID controller was determined based on the control performance such as rise time, settling time and percentage of overshoot. The RGS input signal with sixth number of orders was chosen as the best NARX model estimation because of highest percentage of best fit and lowest value of MSE and FPE from others model. Takahashi tuning method with PRBS input signal had delivered the best control performance with the fastest rise time of 880. 0209s, settling times of 5.1097x10³ s and the lowest percentage of overshoot of 5.0134%. This controller was then chosen to undergo the fine-tuning method, which produced an excellent result with the elimination of system overshoot and a fast response time.