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Unit

Automatic Control Laboratory 3

Laboratory

Summary

The Automatic Laboratory 3 at EPFL, also known as the Predictive Control Lab, focuses on pushing the boundaries of optimization and predictive control methods. They develop data-driven control techniques for applications such as robotics, autonomous driving, rocketry, dynamic flight, and energy consumption management in buildings. The lab's research includes stability analysis, self-triggered control, and real-time optimization algorithm design. They offer student projects at the bachelor and master levels, with a current project focusing on continuous online controller improvement for a mini racecar. The lab also provides teaching in control systems, model predictive control, programming for engineers, and discrete-time control.

Official source

Summary

Official source

Related people (9)

Mengjie Zhao

Mengjie Zhao holds degrees in Computational Mechanics (MSc) with honors track and in Engineering Science (BSc) from the Technical University of Munich (TUM). From the early years of her studies, Mengjie was fascinated by the modeling of multiphysics and multiscale systems. As a student research assistant at TUM and research intern at International Centre for Numerical Methods in Engineering (CIMNE), she gained a solid understanding of both the theoretical and algorithmic fundamentals as well as a wide range of applications. Through the BGCE project with the Elitenetzwerk Bayern (ENB), which dealt with the mesh sensitivity prediction with a deep neural network, she realized that leveraging data could bring physical modeling far beyond the current computational limits. Later, in her master's thesis in cooperation with Siemens, she turned to the reduced-order modeling with enforced physical invariants, which showed better accuracy and generality. In her Ph.D., she would like to step towards a further combination of deductive research (modeling and simulation) and inductive (data-driven) research by embedding physics into machine learning.

Philippe Müllhaupt

Christophe Salzmann

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints