Robust PID Auto-tuning for the Quadruple Tank System

Abstract

In multi-modular process architectures with independent but interacting subsystems, identification may not be the first choice at hand for closed loop control. A robust relay-based PID autotuning strategy is presented and validated on a quadruple tank system with non-minimum phase dynamics. The controller ensures a specified closed loop robustness, which is of great benefit to the overall performance. The experimental results suggest that the proposed method fulfils the robustness requirement and performs well in various operating conditions of the testbench.

A methodology for control structure adaptation in presence of varying, unknown sub-system interaction degree

“…In this case the sub-systems have weak interaction, which can be dealt with using decentralized control. This controller is designed via any procedure, but industrial applications commonly employ CAD tools for tuning the respective K p , T i and T d parameter values, or automatic PID tuners [15]. This decentralized closed loop scheme runs from time instant t 0 until time instant t 1 .…”

“…As soon as the performance index degrades, multivariable iterative PID tuning rules, as given in [15] can be executed. The tuning can be re-executed to maintain performance in presence of varying dynamics of MIMO loops.…”

“…The convergence of the algorithm is established when the output magnitude and phase values in the relay+delay test vary less than 5% from those obtained in the relay test. A detailed description of the auto-tuning procedure is given in [15].…”

“…As an illustrative example, the case of a quadruple water tank system as in the Quanser benchmark (www.Quanser.com), described in [15] is used. The The performed experiment according with the conceptual description from figure 1 is the following:…”

Design and simulation of a normalized fuzzy logic controller for the quadruple-tank process

“…In the past decade, many mechanisms and control techniques have been used to build QTP controllers. These techniques include the predictive model controller [3][4][5][6][7], multiparametric quadratic programming-based controller [8], loop-shaping controller [9], fractional-order proportional-integralderivative (PID) controller [10][11][12][13][14] , fractional-order sliding-mode controller [15,16] , nonlinear backstepping controller with an adaptive high-gain observer [17], decoupling multivariable systems based on generalized predictive control [18], robust relay-based PID auto-tuning strategy [19], fuzzy aggregated multiparametric model predictive controller [20], novel self-tuning dual-mode adaptive fractional-order proportional-integral (PI) controller with an adaptive feedforward controller [21], and nonlinear state feedback decoupling method [22].…”