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Structured learning of safety guarantees for the control of uncertain dynamical systems
In this article, we propose the safe uncertainty learning principle, and argue that the learning must be properly structured to preserve safety guarantees.
Learning-based synthesis of robust linear time-invariant controllers
Recent advances in learning for control allow to synthesize vehicle controllers from learned system dynamics and maintain robust stability guarantees.
Improving gearshift controllers for electric vehicles with reinforcement learning
During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench.
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