Swinging Up and Balancing a Pendulum on a Vertically Moving Cart Using Reinforcement Learning

Vamsi A, Poorna Hima and Ratolikar, Mangesh D and Kumar R, R Prasanth (2021) Swinging Up and Balancing a Pendulum on a Vertically Moving Cart Using Reinforcement Learning. In: IEEE International Conference on Robotics and Biomimetics, ROBIO 2021, 27 December 2021through 31 December 2021, Sanya.

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Abstract

Underactuated systems occur frequently in robotics and legged locomotion. Unactuated pendulum on an actuated cart is a classic example used for designing and testing control algorithms for underactuated systems. While pendulum balancing on a horizontally moving cart is popular and environments available for reinforcement learning, pendulum on vertically moving cart is rarely discussed due to relatively higher difficulty level in balancing it. This paper presents a model environment for a pendulum on a vertically moving cart and trains a neural network controller using reinforcement learning to balance it in vertical position indefinitely without exceeding the displacement limits. Results presented for both con-tinuous and discrete force control input for the cart system show that the neural network controllers can successfully swing up and balance the pendulum. © 2021 IEEE.

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IITH Creators:

IITH Creators	ORCiD
Kumar, R Prasanth	UNSPECIFIED

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Cart system; Control inputs; Legged locomotion; Modeling environments; Neural network controllers; Reinforcement learnings; Robotic locomotions; Unactuated; Under-actuated systems; Vertical positions

Subjects:

Physics > Mechanical and aerospace

Divisions:

Department of Mechanical & Aerospace Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

01 Oct 2022 10:37

Last Modified:

01 Oct 2022 10:40

URI:

http://raiithold.iith.ac.in/id/eprint/10763