Quadratic Model-Based Dynamically Updated PID Control of CSTR System with Varying Parameters

Abstract

In this paper, we discuss an improved version of the conventional PID (Proportional–Integral–Derivative) controller, the Dynamically Updated PID (DUPID) controller. The DUPID is a control solution which preserves the advantages of the PID controller and tends to improve them by introducing a quadratic error model in the PID control structure. The quadratic error model is constructed over a window of past error points. The objective is to use the model to give the conventional PID controller the awareness needed to battle the effects caused by the variation of the parameters. The quality of the predictions that the model is able to deliver depends on the appropriate selection of data used for its construction. In this regard, the paper discusses two algorithms, named 1D (one dimensional) and 2D (two dimensional) DUPID. Appropriate to their names, the former selects data based on one coordinate, whereas the latter selects the data based on two coordinates. Both these versions of the DUPID controller are compared to the conventional PID controller with respect to their capabilities of controlling a Continuous Stirred Tank Reactor (CSTR) system with varying parameters in three different scenarios. As a quantifying measure of the control performance, the integral of absolute error (IAE) metric is used. The results from the performed simulations indicated that the two versions of the DUPID controller improved the control performance of the conventional PID controller in all scenarios.

A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

“…By the results comparing, it is shown that FLS based PID has a much better performance in tracking than the conventional PIDs. In [24], the dynamically updated PID is suggested to control the CSTR units under variable parameterise. The gray wolf optimization scheme is suggested in [25] for PID tuning, and it is shown that the suggested tuning scheme gives better control accuracy than genetic and PSO algorithms.…”

Extended State Observer Based Robust Feedback Linearization Control Applied to an Industrial CSTR

“…In search of new advanced control schemes, theories have evolved in several directions, giving a very rich bibliography over 80 years. For the CSTR control example, various control strategies, such as the exact feedback linearization control [11,12], the nonlinear backstepping control [2], the model predictive control [4,[13][14][15][16][17][18][19], different optimal control strategies [20][21][22][23], the adaptive control approaches [24][25][26][27], and the sliding mode control theory [1,[28][29][30][31][32] have been proposed among others. We can also ind several articles based on successful combinations between advanced nonlinear control theories and soft computing tools such as artiicial neural networks (ANN) [33,34], fuzzy inference systems (FIS) [3,35], and many bio-inspired optimization algorithms such as the genetic algorithm (GA) [7,36], etc.…”