Nonlinear model predictive control with moving horizon state and disturbance estimation - With application to MSW combustion

Abstract This paper presents a nonlinear model predictive control (NMPC) strategy that can be used to tackle model predictive control problems that involve relatively simple nonlinear dynamic models, as for example obtained with first-principles modeling. The main feature of the proposed NMPC strategy is the usage of a moving horizon estimator (MHE) for the estimation of the states and disturbances (and, if desired, parameters). The closed-loop performance properties of the proposed NMPC strategy are demonstrated by applying it to a model of a municipal solid waste (MSW) combustion plant under a realistic disturbance realization. In addition, a comparison is made with extended Kalman filter (EKF) based NMPC.