Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees
Abstract
The combination of machine learning with control offers many opportunities, in particular for robust control. However, due to strong safety and reliability requirements in many real-world applications, providing rigorous statistical and control-theoretic guarantees is of utmost importance, yet difficult to achieve for learning-based control schemes. We present a general framework for learning-enhanced robust control that allows for systematic integration of prior engineering knowledge, is fully compatible with modern robust control and still comes with rigorous and practically meaningful guarantees. Building on the established Linear Fractional Representation and Integral Quadratic Constraints framework, we integrate Gaussian Process Regression as a learning component and state-of-the-art robust controller synthesis. In a concrete robust control example, our approach is demonstrated to yield improved performance with more data, while guarantees are maintained throughout.
Authors 3
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RWTH Aachen University · Max Planck Institute for Intelligent Systems
Affiliation as printed
Institute for Data Science in Mechanical Engi- neering , RWTH Aachen University , Aachen , Germany
Intelligent Control Systems Group , Max Planck Institute for Intelligent Systems , Stuttgart , Germany
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Affiliation as printed
Department of Mathematics , University of Stuttgart , Stuttgart , Germany
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RWTH Aachen University · Max Planck Institute for Intelligent Systems
Affiliation as printed
Institute for Data Science in Mechanical Engi- neering , RWTH Aachen University , Aachen , Germany
Intelligent Control Systems Group , Max Planck Institute for Intelligent Systems , Stuttgart , Germany
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