Flexible model predictive control based on multivariable online adjustment mechanism for robust gait generation

Sheng Dong, Zhaohui Yuan, Xiaojun Yu, Muhammad Tariq Sadiq, Jianrui Zhang, Fuli Zhang, Cheng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

The gait generation algorithm considering both step distance adjustment and step duration adjustment could improve the anti-disturbance ability of the humanoid robot, which is very important to the dynamic balance, but the step duration adjustment often brings non-convex optimization problems. In order to avoid this situation and improve the robustness of the gait generator, a gait generation mechanism based on flexible model predictive control is proposed in this article. Specifically, the step distance adjustment and step duration adjustment are set to be optimization objectives, while the change of pressure center is treated as the optimal input to minimize those objectives. With the current system state being used for online re-optimization, a feedback gait generator is formed to realize the strong stability of variable speed and variable step distance walking of the robot. The main contributions of this work are twofold. First, a gait generation mechanism based on flexible model predictive control is proposed, which avoids the problem of nonlinear optimization. Second, a variety of feasible optimization constraints were considered, they can be used on platforms with different computing resources. Simulations are conducted to verify the effectiveness of the proposed mechanism. Results show that as compared with those considering step adjustment only, the proposed method largely improves the compensation ability of disturbance and shortens the adjustment time.
Original languageEnglish
JournalInternational Journal of Advanced Robotic Systems
Volume17
Issue number1
DOIs
Publication statusPublished - 6 Jan 2020

Bibliographical note

Funding Information:
The authors would like to acknowledge the financial support provided by China Postdoctoral Science Foundation (grant no. 2018M641013), the Natural Science Basic Research Plan in Shaanxi Province of China (program no. 2018JQ6014), and the Fundamental Research Funds for the Central Universities (grant no. G2018KY0308). Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by China Postdoctoral Science Foundation (grant no. 2018M641013), the Natural Science Basic Research Plan in Shaanxi Province of China (program no. 2018JQ6014), and the Fundamental Research Funds for the Central Universities (grant no. G2018KY0308). ORCID iD Sheng Dong https://orcid.org/0000-0001-9302-4893

Funding Information:
The authors would like to acknowledge the financial support provided by China Postdoctoral Science Foundation (grant no. 2018M641013), the Natural Science Basic Research Plan in Shaanxi Province of China (program no. 2018JQ6014), and the Fundamental Research Funds for the Central Universities (grant no. G2018KY0308). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by China Postdoctoral Science Foundation (grant no. 2018M641013), the Natural Science Basic Research Plan in Shaanxi Province of China (program no. 2018JQ6014), and the Fundamental Research Funds for the Central Universities (grant no. G2018KY0308).

Publisher Copyright:
© The Author(s) 2019.

Keywords

  • adaptive step duration
  • gait generation
  • Humanoid robots
  • model predictive control

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