Controller design based on Model Predictive Control for a nonlinear process

Abstract

Nowadays process industries require accurate, efficient and flexible operation of the plants. The need for development of innovative technologies for process modeling, dynamic trajectory optimization and high performance industrial process control is always a challenge. The process considered for modeling is a conical tank liquid level system. Control of liquid level in a conical tank is nonlinear due to the variation in the area of cross section with change in shape. Black box modeling is used to identify the system, which is identified to be nonlinear and approximated to be a First Order Plus Dead Time (FOPDT) model. Here the controller design is compared based on conventional Proportional Integral (PI) based on Skogestad's settings with Model Predictive Control (MPC).