Control of the Tennessee Eastman process using input-output models

This paper presents a case-study where model predictive control is applied to control a nonlinear, open-loop unstable process called the Tennessee Eastman Challenge Process. Both the base case and transitions between different operating points are considered. The control scheme is based on an inputoutput model identified from plant data. The Model Predictive Controller (MPC) controller acts as a supervisory controller that dictates the setpoints for a lower level PID loop structure. Simulations are presented to illustrate its effectiveness or disturbance rejection and setpoint tracking.