Intelligent feedforward control and payload estimation for a two-link robotic manipulator

Conventional model-based computed torque control fails to produce a good trajectory tracking performance in the presence of payload uncertainty and modeling error. The challenge is to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural-netwo...

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Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2003-06, Vol.8 (2), p.277-282
Main Authors: Nho, H.C., Meckl, P.
Format: Article
Language:English
Subjects:
Applied sciences
Architecture
Articulated
Artificial intelligence
Computer science
control theory
systems
Control theory. Systems
Drives
Exact sciences and technology
Intelligent control
Intelligent robots
Learning and adaptive systems
Linkage mechanisms, cams
Manipulator dynamics
Manipulators
Mechanical engineering. Machine design
Multi-layer neural network
Neural networks
Payloads
PD control
Proportional control
Robot control
Robotics
Robots
Torque control
Tracking
Trajectory
Uncertainty
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Summary:Conventional model-based computed torque control fails to produce a good trajectory tracking performance in the presence of payload uncertainty and modeling error. The challenge is to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural-network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. An off-line trained feedforward (multilayer) neural network takes payload mass estimates from a fuzzy-logic mass estimator as one of the inputs to represent the inverse dynamics of the articulated robot. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experimental results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2003.812847