1School of Electrical and Electronics Engineering, Shanmugha Arts Science Technology Research Academy (SASTRA), Tirumalaisamudram, Thanjavur, TamilNadu, India-613 402.
2Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirapalli, Tamil Nadu, India-620 015.
3Department of Physics, Department of Chemical Engineering, National Institute of Technology, Tiruchirapalli, Tamil Nadu, India-620 015.
Nonlinear processes abound in the chemical industry. Although linear controllers work for some of these systems, processes that contain highly nonlinear behavior typically require a method that takes these nonlinearities into account. Thus, in the last few years there has been an emphasis on nonlinear control strategies based on the model. Therefore, it is of great importance to have an adequate model for the nonlinear process, and this model must be identified from process data in many cases. In this paper a control concept of two nonlinear interacting processes is taken. The model identification is done using black-box method which is identified to be second order system, and then reduced to First Order Plus Dead Time (FOPDT) model using Skogestad's half rule. In this study, the designed controllers are based on Skogestad's tuning with Fuzzy Logic Controller (FLC). The controller was tested on the real time environment of conical and spherical tank which is connected in interaction by connecting to an ADAM's data acquisition module. The results indicate that the intelligent controller of FLC outperforms the conventional PI controller based on performance indices comparison like Integral Squared Error (ISE) and Integral Average Error (IAE).
Interacting System, PI Controller, Fuzzy Logic Controller, Modeling and Non-Linear System
