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Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions

Grandia, Ruben and Taylor, Andrew J. and Singletary, Andrew and Hutter, Marco and Ames, Aaron D. (2020) Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions. In: Robotics: Science and Systems XVI. , Art. No. 098. https://resolver.caltech.edu/CaltechAUTHORS:20200707-104619738

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Abstract

The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while ensuring stability guarantees. In this paper we present the first real-time realization of a unified NMPC and CLF controller on a robotic system with limited computational resources. These limitations motivate a set of approaches for efficiently incorporating CLF stability constraints into a general NMPC formulation. We evaluate the performance of the proposed methods compared to baseline CLF and NMPC controllers with a robotic Segway platform both in simulation and on hardware. The addition of a prediction horizon provides a performance advantage over CLF based controllers, which operate optimally point-wise in time. Moreover, the explicitly imposed stability constraints remove the need for difficult cost function and parameter tuning required by NMPC. Therefore the unified controller improves the performance of each isolated controller and simplifies the overall design process.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://www.roboticsproceedings.org/rss16/p098.htmlOrganizationArticle
https://arxiv.org/abs/2006.01229arXivDiscussion Paper
ORCID:
AuthorORCID
Grandia, Ruben0000-0002-8971-6843
Taylor, Andrew J.0000-0002-5990-590X
Singletary, Andrew0000-0001-6635-4256
Hutter, Marco0000-0001-9049-534X
Ames, Aaron D.0000-0003-0848-3177
Additional Information:R. Grandia and M. Hutter are supported via the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780883. A. Taylor, A. Singletary, and A. Ames are supported via DARPA awards HR00111890035 and NNN12AA01C, and NSF awards 1923239 and 1924526.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)780883
Defense Advanced Research Projects Agency (DARPA)HR00111890035
NASANNN12AA01C
NSFCMMI-1923239
NSFECCS-1924526
Record Number:CaltechAUTHORS:20200707-104619738
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200707-104619738
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104245
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Jul 2020 18:01
Last Modified:16 Oct 2020 17:22

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