Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

Liyang Wang, Ming Chen, Gai Li and Yongqing Fan

Mathematical Problems in Engineering, 2016, vol. 2016, 1-19

Abstract:

Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR-) based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP) stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF) is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF). Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2016/1984634.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2016/1984634.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1984634

DOI: 10.1155/2016/1984634

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().