Robust model predictive control of linear finite impulse response plants
Master of Science
Model Predictive Control (MPC) has become one of the dominant methods of chemical process control in terms of successful industrial applications. A rich theory has been developed to study the closed loop stability of MPC algorithms when the plant model is perfect, referred to as the nominal stability problem. In practical applications, however, the process model is never perfect and nominal stability results are not strictly applicable. The primary disadvantage of the current design techniques for MPC is their inability to deal explicitly with the plant model uncertainty. In this thesis we develop a new framework for robust MPC synthesis that allows explicit incorporation of the plant uncertainty description in the problem formulation. Model uncertainty is parameterized by ellipsoid bounds on the Finite Impulse Response (FIR) parameters of the plant model. Robust stability is achieved through the addition of constraints that prevent the sequence of the optimal controller costs from increasing for the true plant. The framework developed here can also be used for constant output disturbance rejection. Ward input and soft output constraints can be easily added to the algorithms without affecting the closed loop stability properties.