DESIGN OF ROBUST, MULTIVARIABLE INTERNAL MODEL CONTROL (IMC) FOR QUADRUPLE TANK PROCESS

Abstract

Many industrial control problems are nonlinear and multivariable in nature. It is common for models of industrial processes to have significant uncertainties, strong interactions, and/or non-minimum phase behavior (i.e., right half plane transmission zeros). The Quadruple tank process also exhibits elegantly complex dynamics which emerge from a simple cascade of tanks. Such dynamic characteristics include interactions and transmission zero location that are tunable in operation. The four tank system has been designed to illustrate both traditional and advanced multivariable control strategies and has been utilized as an educational tool in teaching advanced multivariable control techniques. The specific experiment presented in this study introduces a number of nonidealities including gravitational heads, piping friction losses in addition to behaviors such as vortices and splashing effects on level measurement, etc. These characteristics, combined with the complex interactive behavior, enable the four tank system to serve as a valuable educational tool as well as provide interesting challenges in the control design. The process variations include uncertainties in the actuators and valve settings. The objective of the present study is to design a robustly performing controller that provides stable and acceptable performance for flow set point tracking. In the present work by using the above apparatus, we have designed a robust multivariable internal model controller to consider uncertainties in the process parameters (valve constants, flow ratios, etc). In this work, we used Multivariable Internal Model Control (IMC) technique to design the robust controller for a simulated quadruple tank process. In this work, we redesigned the Decentralized Proportional Integral (DPI) controller, Decoupled Proportional Integral (DCPI) controller using simulink of Matlab®. The performances of these three controllers were compared. It was found that the Multivariable IMC controller performs better than the other Control structures. Tuning parameters were identified and simulated responses of the proposed controllers were presented by using MATLAB tools such as Simulink.