Multiple model-based control of robotic manipulators: theory and experimentation

The multiple-model-based control (MMBC) technique utilizes knowledge of nominal robot dynamics and principles of Bayesian estimation to provide payload-independent trajectory tracking accuracy. The MMBC algorithm is formed by augmenting a model-based controller with a form of multiple-model adaptive...

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Bibliographic Details
Published inProceedings. 5th IEEE International Symposium on Intelligent Control 1990 pp. 830 - 835 vol.2
Main Authors Leahy, M.B., Sablan, S.J.
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1990
Subjects
Adaptive control
Bayesian methods
Control systems
Force control
Manipulators
Motion control
Payloads
Robot control
Service robots
Trajectory
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Summary:The multiple-model-based control (MMBC) technique utilizes knowledge of nominal robot dynamics and principles of Bayesian estimation to provide payload-independent trajectory tracking accuracy. The MMBC algorithm is formed by augmenting a model-based controller with a form of multiple-model adaptive estimation (MMAE). The MMAE uses perturbation models of the robot dynamics and joint angle measurements to provide an estimate of the payload parameters required to minimize trajectory tracking errors. The model-based controller combines the a priori knowledge of robot structure with the payload estimate to produce the multiple models of the manipulator dynamics required to maintain controller accuracy. The development of the PUMA-specific version of the MMBC is presented first three links of PUMA-560, along with experimental validation of extensive simulation studies.< >
ISBN:0818621087
9780818621086
ISSN:2158-9860
2158-9879
DOI:10.1109/ISIC.1990.128553