Open document
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General information |
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Abdessemed, Foudil |
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InTech Open Access Publisher, 2012
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| Abstract |
Real systems are usually non‐linear, ill‐defined,
have variable parameters and are subject to external
disturbances. Modelling these systems is often an
approximation of the physical phenomena involved.
However, it is from this approximate system of
representation that we propose ‐ in this paper ‐ to build a
robust control, in the sense that it must ensure low
sensitivity towards parameters, uncertainties, variations
and external disturbances. The computed torque method
is a well‐established robot control technique which takes
account of the dynamic coupling between the robot links.
However, its main disadvantage lies on the assumption
of an exactly known dynamic model which is not
realizable in practice. To overcome this issue, we propose
the estimation of the dynamics model of the nonlinear
system with a machine learning regression method. The
output of this regressor is used in conjunction with a PD
controller to achieve the tracking trajectory task of a robot
manipulator. In cases where some of the parameters of
the plant undergo a change in their values, poor
performance may result. To cope with this drawback, a
fuzzy precompensator is inserted to reinforce the SVM
computed torque‐based controller and avoid any
deterioration. The theory is developed and the simulation
results are carried out on a two‐degree of freedom robot
manipulator to demonstrate the validity of the proposed
approach. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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