Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference

Wankun Sirichotiyakul, Nardos Ayele Ashenafi, Aykut C. Satici

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

We synthesize controllers for underactuated robotic systems using data-driven approaches. Inspired by techniques from classical passivity theory, the control law is parametrized by the gradient of an energy-like (Lyapunov) function, which is represented by a neural network. With the control task encoded as the objective of the optimization, we systematically identify the optimal neural net parameters using gradient-based techniques. The proposed method is validated on the cart-pole swing-up task, both in simulation and on a real system. Additionally, we address questions about controller’s robustness against model uncertainties and measurement noise, using a Bayesian approach to infer a probability distribution over the parameters of the controller. The proposed robustness improvement technique is demonstrated on the simple pendulum system.

Original languageAmerican English
Title of host publication2022 American Control Conference (ACC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3266-3272
Number of pages7
ISBN (Electronic)9781665451963
DOIs
StatePublished - 1 Jan 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: 8 Jun 202210 Jun 2022

Publication series

Name0743-1619

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period8/06/2210/06/22

Keywords

  • control systems
  • neural networks
  • probability distribution
  • robustness
  • training
  • uncertainty

EGS Disciplines

  • Biomedical Engineering and Bioengineering
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference'. Together they form a unique fingerprint.

Cite this