General

• Stability-oriented learning in data-driven control for safety guarantees
• Stability analysis techniques and probablistic uncertainty-handling methods
• Nonlinear systems, soft robotics, aerodynamics
• Passivity-based control
• Gaussian processes

Motivation

Lagrangian systems comprise a large class of physical systems such as robots, aircrafts or marine vehicles. The application of Gaussian Processes (GPs) in learning-based control has the potential to significantly improve criteria such as performance and safety. However, in general GP regression does not account for key problems such as:

• physical consistency, i.e., the fulfillment of the fundamental underlying physical properties,
• the trade-off between physics-imposed symmetries and model flexibility,
• reliable and robust applicability in the control of uncertain physical/passive systems,
• computational efficiency and data-efficiency.

Approach

Our main approach is to construct & apply physically consistent data-driven models, so-called Lagrangian-Gaussian Processes, to identify complex nonlinear, high- or inifinite-dimensional, and coupled dynamics. We exploit energy components and the differential equation structure to consistently model these systems in an efficient manner. We account for inherent robustness in the learning as well as the control phases, and for physical intuition of the data-driven model, by making use of structurally preserving passivity-based control methods, minimally changing and exploiting the dynamics, instead of, e.g., canceling all nonlinearities to achieve linear dynamics.

Acknowledgment

This work is supported by the Consolidator Grant ”Safe data-driven control for human-centric systems” (CO-MAN) of the European Research Council (ERC) under grant agreement ID 864686.