Multi-scale aspects in model-predictive control

Multi-scale models of processing systems offer an attractive alternative to models defined in the time- or frequency-domain. They are defined on dyadic or higher-order trees, whose nodes are used to index the values of any variable, localized in both time and scale (range of frequencies). This dual localization is particularly attractive in solving estimation and control problems. In this paper, multi-scale models are used to design model-predictive controllers (MPC), resulting in design techniques with several important advantages, such as; (a) natural depiction of performance characteristics and treatment of output constraints, (b) fast algorithms for establishing the constrained control policies over long prediction/control horizons, (c) rich depiction of feedback errors at several scales, and (d) optimal fusion of multi-rate measurements and control actions..