Invention of Prediction Structures and Categorization of Robust MPC Syntheses

Abstract

This plenary paper is concerned with robust model predictive control (MPC) synthesis. In particular, the novel notion of prediction structures is introduced, and then utilized to derive a precise and compact overview of the existing robust MPC (RMPC) syntheses as well as to indicate direct improvements of their system theoretic properties. The prediction structures paradigm allows for a systematic, implementation and examples independent, comparison and classification of the existing RMPC syntheses. The corresponding categorization of the currently available RMPC syntheses is derived by: (i) introducing the adequate indices as measures of structural (including topological and system theoretic) properties and computational complexity, and (ii) analysing the trade–off between the guaranteed structural properties and the necessary computational complexity. The associated analysis is based on the classical game and utility theory notions; for simplicity, it is delivered by deploying the aggregated structural property and computational complexity indices; furthermore, it is complemented with an unambiguous analysis of the “holy–grail” trade–off between the quality of structural properties and the degree of computational complexity.