Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning

TL;DR

This paper presents a sample-efficient reinforcement learning approach to control DC motors in real-time for micromobility, achieving rapid learning both in simulation and on physical hardware.

Contribution

It introduces a reinforcement learning method that learns effective DC motor control policies quickly using real-world data and simulation, reducing training time compared to existing approaches.

Findings

Achieved control policy learning in under 2 minutes in simulation.

Learned effective control policy in approximately 10 minutes on real hardware.

Demonstrated robustness and efficiency in controlling non-linear DC motors.

Abstract

Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years. A key component of many micro-transport vehicles is the DC motor, a complex dynamical system that is continuous and non-linear. Learning to quickly control the DC motor in the presence of disturbances and uncertainties is desired for various applications that require robustness and stability. Techniques to accomplish this task usually rely on a mathematical system model, which is often insufficient to anticipate the effects of time-varying and interrelated sources of non-linearities. While some model-free approaches have been successful at the task, they rely on massive interactions with the system and are trained in specialized hardware in order to fit a highly parameterized controller. In this work, we learn to steer a DC motor via sample-efficient reinforcement learning. Using data collected from hardware interactions in the real world, we additionally build a simulator to experiment with a wide range of parameters and learning strategies. With the best parameters found, we learn an effective control policy in one minute and 53 seconds on a simulation and in 10 minutes and 35 seconds on a physical system.