Reinforcement Learning for Active Damping of Harmonically Excited Pendulum with Highly Nonlinear Actuator

Published

Conference Paper

© 2020, Society for Experimental Mechanics, Inc. Active vibration dampers can reduce or eliminate unwanted vibrations, but determining a good control policy can be challenging for highly nonlinear systems. For these types of systems, reinforcement learning is one method to optimize a control policy with only limited prior knowledge of the system dynamics. An experimental system was constructed by attaching a permanent magnet to the end of a pendulum and positioning an electromagnetic actuator below the resting position of the pendulum. The pendulum was excited with a sinusoidal force applied horizontally at the pivot point, and the control input was the applied voltage across the electromagnet. Due to the geometric arrangement and the strong dependence of magnetic force on distance, the relationship between the position of the pendulum and the actuation torque for any control input was highly nonlinear. A generalized version of the PILCO reinforcement learning algorithm was used to optimize a control policy for the electromagnet with the objective of minimizing the distance between the end of the pendulum and the downward position. After 16 s of interaction with the experimental system, the resulting learned policy was able to substantially reduce the amplitude of oscillation. This experiment illustrates the applicability of reinforcement learning to highly nonlinear active vibration damping problems.

Full Text

Duke Authors

Cited Authors

  • Turner, JD; Manring, LH; Mann, BP

Published Date

  • January 1, 2020

Published In

Start / End Page

  • 119 - 123

Electronic International Standard Serial Number (EISSN)

  • 2191-5652

International Standard Serial Number (ISSN)

  • 2191-5644

International Standard Book Number 13 (ISBN-13)

  • 9783030123901

Digital Object Identifier (DOI)

  • 10.1007/978-3-030-12391-8_15

Citation Source

  • Scopus