Nonlinear Model Predictive Control Based on a Nonlinear State Observe

SILVINA INES BIAGIOLA; JOSE LUIS FIGUEROA

San Nicolas

Workshop; X Reunión de trabajo en Procesamiento de la Información y Control; 2003

Universidad Tecnologica Nacional

Model predictive control (MPC) has become very popular both in process industry and academia due to its effectiveness in dealing with nonlinear, multivariable and/or hard constrained plants. Although linear MPC can be applied for controlling nonlinear processes by obtaining a linearized model of the plant, this is only valid in a limited region. The purpose of this paper is to introduce a nonlinear model predictive controller (NMPC) in order to exploit the knowledge of the nonlinear dynamics and to avoid modeling simplifications  or linearization. In order to up date the optimization involved in NMPC strategy, state estimation based on the measured outputs is proposed. As an illustrative case, an open-loop unstable nonlinear reactor is dealt with. Effectiveness in dealing with nonlinear, multivariable and/or hard constrained plants. Although linear MPC can be applied for controlling nonlinear processes by obtaining a linearized model of the plant, this is only valid in a limited region. The purpose of this paper is to introduce a nonlinear model predictive controller (NMPC) in order to exploit the knowledge of the nonlinear dynamics and to avoid modeling simplifications  or linearization. In order to up date the optimization involved in NMPC strategy, state estimation based on the measured outputs is proposed. As an illustrative case, an open-loop unstable nonlinear reactor is dealt with.