Nonlinear predictive control of an industrial power plant boiler

This paper proposes an efficient nonlinear predictive controller (NMPC) for application to a power plant drum-boiler. The control objectives are to maintain the water and pressure levels in the drum within a desired range. First, the nonlinear model of the drum-boiler is transformed to LTV state-dependent nonlinear form to provide global nonlinear behavior. Next, state-dependent nonlinear Kalman filter is used to estimate the system states. Then, a supervisory NMPC algorithm is used as a second level controller to generate optimal set points to the lower level regulating PID loops while maintaing output constraints. Simulation results are presented to demonstrate the excellent tracking and disturbance rejection performance compared with a stand-alone multi loop PID controllers.