Model Identification and Control Design for a Humanoid Robot

Wei He; Weiliang Ge; Yunchuan Li; Yan Jun Liu; Chenguang Yang; Changyin Sun

doi:10.1109/TSMC.2016.2557227

Model Identification and Control Design for a Humanoid Robot

Wei He
, Weiliang Ge
, Yunchuan Li
, Yan Jun Liu
, Chenguang Yang
, Changyin Sun

Research output: Contribution to journal › Article › peer-review

150 Scopus citations

Abstract

In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.

Original language	English
Article number	7469783
Pages (from-to)	45-57
Number of pages	13
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	47
Issue number	1
DOIs	https://doi.org/10.1109/TSMC.2016.2557227
State	Published - Jan 2017
Externally published	Yes

Keywords

Adaptive control
humanoid robot
model identification
particle swarm optimization (PSO)
recursive Newton-Euler (RNE) formulation

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10.1109/TSMC.2016.2557227

Cite this

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title = "Model Identification and Control Design for a Humanoid Robot",

abstract = "In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.",

keywords = "Adaptive control, humanoid robot, model identification, particle swarm optimization (PSO), recursive Newton-Euler (RNE) formulation",

author = "Wei He and Weiliang Ge and Yunchuan Li and Liu, \{Yan Jun\} and Chenguang Yang and Changyin Sun",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2017",

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doi = "10.1109/TSMC.2016.2557227",

language = "英语",

volume = "47",

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T1 - Model Identification and Control Design for a Humanoid Robot

AU - He, Wei

AU - Ge, Weiliang

AU - Li, Yunchuan

AU - Liu, Yan Jun

AU - Yang, Chenguang

AU - Sun, Changyin

PY - 2017/1

Y1 - 2017/1

N2 - In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.

AB - In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.

KW - Adaptive control

KW - humanoid robot

KW - model identification

KW - particle swarm optimization (PSO)

KW - recursive Newton-Euler (RNE) formulation

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DO - 10.1109/TSMC.2016.2557227

M3 - 文章

AN - SCOPUS:85007441129

SN - 2168-2216

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JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

IS - 1

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ER -

Model Identification and Control Design for a Humanoid Robot

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Keywords

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