The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control

TL;DR

This paper presents a detailed design and open-source implementation of a multi-arm pendulum on a cart system, serving as a versatile benchmark for chaos, learning, and control research in dynamical systems.

Contribution

It provides comprehensive construction details and open-source resources for a high-performance multi-arm pendulum system, facilitating reproducible research and remote operation capabilities.

Findings

Enables diverse experiments in chaos and control.

Supports system identification and learning research.

Accessible design promotes widespread use and collaboration.

Abstract

The single, double, and triple pendulum has served as an illustrative experimental benchmark system for scientists to study dynamical behavior for more than four centuries. The pendulum system exhibits a wide range of interesting behaviors, from simple harmonic motion in the single pendulum to chaotic dynamics in multi-arm pendulums. Under forcing, even the single pendulum may exhibit chaos, providing a simple example of a damped-driven system. All multi-armed pendulums are characterized by the existence of index-one saddle points, which mediate the transport of trajectories in the system, providing a simple mechanical analog of various complex transport phenomena, from biolocomotion to transport within the solar system. Further, pendulum systems have long been used to design and test both linear and nonlinear control strategies, with the addition of more arms making the problem more challenging. In this work, we provide extensive designs for the construction and operation of a high-performance, multi-link pendulum on a cart system. Although many experimental setups have been built to study the behavior of pendulum systems, such an extensive documentation on the design, construction, and operation is missing from the literature. The resulting experimental system is highly flexible, enabling a wide range of benchmark problems in dynamical systems modeling, system identification and learning, and control. To promote reproducible research, we have made our entire system open-source, including 3D CAD drawings, basic tutorial code, and data. Moreover, we discuss the possibility of extending our system capability to be operated remotely to enable researchers all around the world to use it, thus increasing access.