Reinforcement Learning Framework for the self-learning Suppression of Clutch Judder in automotive Drive Trains

In electromechanically actuated clutches, the active damping of vibrations by means of control of the clamping force allow the use of high performance materials in the friction pairing, which makes a more energy and cost efficient design of the clutch. In this work, a reinforcement learning framework for the control of the clamping force for the active suppression of judder vibrations is proposed and developed