Anytime algorithms for estimation-based model predictive control

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In the course of the continuously advancing digitization, the systematic and efficient analysis of a big amount of measurement data, particularly by means of optimization-based estimation procedures, is one of the big future challenges in the field of automation and control of technical processes. In particular, this applies to modern model-based predictive control approaches, which in many cases require to estimate the current system state and/or other process parameters from measurements in order to predict and optimize the system behavior. However, despite the enormous conceptual and practical relevance, there exist up to now no approaches that allow for a systems theoretically sound and numerically efficient integration of dynamical on-line measurements into model-based predictive control schemes. Motivation and basic idea of this research project is therefore the design and development of a novel class of estimation-based model predictive control procedures, which combine the estimation of relevant state information and process parameters in an integrative and optimization-based framework with a predictive control scheme. In particular, the overall goal is an innovative and systems theoretically sound methodology for the design of so-called anytime algorithms, which allow to ensure important stability and real-time guarantees of estimation and control even in cases where the underlying optimization algorithms perform only a limited number of iterations, for example due to big amounts of measurements or limited computational resources.