ExplicitModelPredictiveControl.PDFVIP

Explicit Model Predictive Control for Large-Scale Systems via Model Reduction Svein Hovland? and Jan Tommy Gravdahl? Norwegian University of Science and Technology, N-7491 Trondheim, Norway and Karen E. Willcox? Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA In this paper we present a framework for achieving constrained optimal real-time control for large-scale systems with fast dynamics. The methodol- ogy uses the explicit solution of the model predictive control (MPC) prob- lem combined with model reduction, in an output-feedback implementation. The explicit solution of the MPC problem leads to online MPC function- ality without having to solve an optimization problem at each time step. Reduced-order models are derived using a goal-oriented, model-constrained optimization formulation that yields e?cient models tailored to the control application at hand. The approach is illustrated on a challenging large- scale ?ow problem that aims to control the shock position in a supersonic di?user. I. Introduction With the increasing interest in ?uid ?ow control over the last decade, there arises a need for control methodology that can achieve constrained optimal real-time control of distrib- uted systems with fast dynamics, such as in mechatronics, MEMS, rotating machinery and acoustics. Computational ?uid dynamic (CFD) models of such systems typically have order ?PhD Student, Department of Engineering Cybernetics; svein.hovland@itk.ntnu.no. ?Professor, Department of Engineering Cybernetics; tommy.gravdahl@itk.ntnu.no. ?Associate Professor, Department of Aeronautics and Astronautics; kwillcox@mit.edu. Associate Fellow AIAA. 1 of 23 exceeding 104, which is prohibit