An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control

Kai Yuan; Zhibin Li

doi:10.1109/IROS.2018.8594309

An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

Predictive control methods for walking commonly use low dimensional models, such as a Linear Inverted Pendulum Model (LIPM), for simplifying the complex dynamics of legged robots. This paper identifies the physical limitations of the modeling methods that do not account for external disturbances, and then analyzes the issues of numerical stability of Model Predictive Control (MPC) using different models with variable receding horizons. We propose a new modeling formulation that can be used for both gait planning and feedback control in an MPC scheme. The advantages are the improved numerical stability for long prediction horizons and the robustness against various disturbances. Benchmarks were rigorously studied to compare the proposed MPC scheme with the existing ones in terms of numerical stability and disturbance rejection. The effectiveness of the controller is demonstrated in both MATLAB and Gazebo simulations.

Original language	English
Title of host publication	Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Place of Publication	Madrid, Spain
Publisher	Institute of Electrical and Electronics Engineers
Pages	8535-8542
Number of pages	8
ISBN (Electronic)	978-1-5386-8094-0
ISBN (Print)	978-1-5386-8095-7
DOIs	https://doi.org/10.1109/IROS.2018.8594309
Publication status	Published - 7 Jan 2019
Event	2018 IEEE/RSJ International Conference on Intelligent Robots and Systems - Madrid, Spain Duration: 1 Oct 2018 → 5 Oct 2018 https://www.iros2018.org/

Publication series

Name
Publisher	IEEE
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	2018 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated title	IROS 2018
Country/Territory	Spain
City	Madrid
Period	1/10/18 → 5/10/18
Internet address	https://www.iros2018.org/

Keywords / Materials (for Non-textual outputs)

Model-Predictive Control
Dynamic Locomotion
Humanoid Robots

Access to Document

10.1109/IROS.2018.8594309

An Improved Formulation for Model Predictive Control of LeggedAccepted author manuscript, 1.55 MB

https://ieeexplore.ieee.org/document/8594309

Cite this

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title = "An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control",

abstract = "Predictive control methods for walking commonly use low dimensional models, such as a Linear Inverted Pendulum Model (LIPM), for simplifying the complex dynamics of legged robots. This paper identifies the physical limitations of the modeling methods that do not account for external disturbances, and then analyzes the issues of numerical stability of Model Predictive Control (MPC) using different models with variable receding horizons. We propose a new modeling formulation that can be used for both gait planning and feedback control in an MPC scheme. The advantages are the improved numerical stability for long prediction horizons and the robustness against various disturbances. Benchmarks were rigorously studied to compare the proposed MPC scheme with the existing ones in terms of numerical stability and disturbance rejection. The effectiveness of the controller is demonstrated in both MATLAB and Gazebo simulations.",

keywords = "Model-Predictive Control, Dynamic Locomotion, Humanoid Robots",

author = "Kai Yuan and Zhibin Li",

year = "2019",

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language = "English",

isbn = "978-1-5386-8095-7",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "8535--8542",

booktitle = "Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",

address = "United States",

note = "2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 ; Conference date: 01-10-2018 Through 05-10-2018",

url = "https://www.iros2018.org/",

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Yuan, K & Li, Z 2019, An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control. in Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Institute of Electrical and Electronics Engineers, Madrid, Spain, pp. 8535-8542, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain, 1/10/18. https://doi.org/10.1109/IROS.2018.8594309

An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control. / Yuan, Kai; Li, Zhibin.
Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: Institute of Electrical and Electronics Engineers, 2019. p. 8535-8542.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control

AU - Yuan, Kai

AU - Li, Zhibin

PY - 2019/1/7

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N2 - Predictive control methods for walking commonly use low dimensional models, such as a Linear Inverted Pendulum Model (LIPM), for simplifying the complex dynamics of legged robots. This paper identifies the physical limitations of the modeling methods that do not account for external disturbances, and then analyzes the issues of numerical stability of Model Predictive Control (MPC) using different models with variable receding horizons. We propose a new modeling formulation that can be used for both gait planning and feedback control in an MPC scheme. The advantages are the improved numerical stability for long prediction horizons and the robustness against various disturbances. Benchmarks were rigorously studied to compare the proposed MPC scheme with the existing ones in terms of numerical stability and disturbance rejection. The effectiveness of the controller is demonstrated in both MATLAB and Gazebo simulations.

AB - Predictive control methods for walking commonly use low dimensional models, such as a Linear Inverted Pendulum Model (LIPM), for simplifying the complex dynamics of legged robots. This paper identifies the physical limitations of the modeling methods that do not account for external disturbances, and then analyzes the issues of numerical stability of Model Predictive Control (MPC) using different models with variable receding horizons. We propose a new modeling formulation that can be used for both gait planning and feedback control in an MPC scheme. The advantages are the improved numerical stability for long prediction horizons and the robustness against various disturbances. Benchmarks were rigorously studied to compare the proposed MPC scheme with the existing ones in terms of numerical stability and disturbance rejection. The effectiveness of the controller is demonstrated in both MATLAB and Gazebo simulations.

KW - Model-Predictive Control

KW - Dynamic Locomotion

KW - Humanoid Robots

U2 - 10.1109/IROS.2018.8594309

DO - 10.1109/IROS.2018.8594309

M3 - Conference contribution

SN - 978-1-5386-8095-7

SP - 8535

EP - 8542

BT - Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

PB - Institute of Electrical and Electronics Engineers

CY - Madrid, Spain

T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems

Y2 - 1 October 2018 through 5 October 2018

ER -

An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control

Abstract / Description of output

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Conference

Keywords / Materials (for Non-textual outputs)

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Cite this