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

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Original languageEnglish
Title of host publicationProceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Number of pages8
StateAccepted/In press - 29 Jun 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018
https://www.iros2018.org/

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2018
CountrySpain
CityMadrid
Period1/10/185/10/18
Internet address

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.

    Research areas

  • Model-Predictive Control, Dynamic Locomotion, Humanoid Robots

Event

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems

1/10/185/10/18

Madrid, Spain

Event: Conference

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