INTERNAL MODEL CONTROL PERFORMANCE EVALUATION IN NONLINEAR COUPLED TANK SYSTEM

Most industrial process control systems are associated with non-linearity behaviour.Control of such industrial process system is a challenging task according to the degree ofnon-linearity of the system. A typical process control that represent part of such complexindustrial process is the principle used in Coupled Tank System (CTS) level control. Thispaper investigated the performance of Neural Network Internal Model Control (NNIMC)and Two Degree of Freedom Internal Model Control (2DOF-IMC) with application to levelcontrol in CTS. For the NNIMC, the Neural Network (NN) forward and inverse models ofthe plant were identified from process input and output sample data generated fromlaboratory experimental setup. The network architecture for both models is nonlinearautoregressive network with exogenous inputs (NARX) while the training algorithm selectedwas Levenberg Marquad (LM). The models developed are then used to design NNIMC.Additionally, the 2DOF-IMC was designed with advanced tracking and disturbancerejection filter. Moreover, conventional PI controller was optimized by integrating neuralnetwork feedforward control strategy to serve as advanced feedforward PI control strategy.Performance analysis of the investigated controls algorithms were evaluated. Simulationresults indicated that both NNIMC and 2DOF-IMC controllers have similar performanceand as well outperforms their traditional control techniques counterpart hence could be usedto control levels in a CTS effectively..