Reinforcement Learning for Active Noise Control in a Hydraulic System

Abstract

Hydraulic pressure ripple in a pump, as a result of converting rotational power to fluid power, continues to be a problem faced when developing hydraulic systems due to the resulting noise generated. In this paper, we present simulation results from leveraging an actor-critic reinforcement learning method as the control method for active noise control in a hydraulic system. The results demonstrate greater than 96%, 81%, and 61% pressure ripple reduction for the first, second, and third harmonics, respectively, in a single operating point test, along with the advantage of feed forward like control for high bandwidth response during dynamic changes in the operating point. It also demonstrates the disadvantage of long convergence times while the controller is effectively learning the optimal control policy. Additionally, this work demonstrates the ancillary benefit of the elimination of the injection of white noise for the purpose of system identification in the current state of the art.