The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Real-time dynamic control of an industrial manipulator using a neural network-based learning controller
Abstract
A learning control technique that uses an extension of the cerebellar model articulation control network developed by J.S. Albus (1975) is discussed, and results of real-time control experiments that involved learning the dynamics of a five-axis industrial robot (General Electric P-5) during high-speed movements are presented. During each control cycle, a training scheme was used to adjust the weights in the network in order to form an approximate dynamic model of the robot in appropriate regions of the control space. Simultaneously, the network was used during each control cycle to predict the actuator drives required to follow a desired trajectory, and these drives were used as feedforward terms in parallel to a fixed-gain linear feedback controller. Trajectory tracking errors were found to converge to low values within a few training trials, and to be relatively insensitive to the choice of control system gains. The effects of network memory size and trajectory characteristics on learning system performance were investigated.>
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Cites
(1991) · Springer eBooks · doi:10.1007/978-3-7091-2840-4 · Cited by 5,095
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Bettering operation of Robots by learning
Arimoto, S., Kawamura, S., & Miyazaki, F. (1984) · Journal of Robotic Systems · doi:10.1002/rob.4620010203 · Cited by 3,474
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,233
On the Adaptive Control of Robot Manipulators
Slotine, J.-J. E., & Li, W. (1987) · The International Journal of Robotics Research · doi:10.1177/027836498700600303 · Cited by 2,284
A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)
Albus, J. S. (1975) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3426922 · Cited by 2,177
(1986) · Signal Processing · doi:10.1016/0165-1684(86)90025-3 · Cited by 1,594
On-Line Computational Scheme for Mechanical Manipulators
Luh, J. Y. S., Walker, M. W., & Paul, R. P. C. (1980) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3149599 · Cited by 1,585
A hierarchical neural-network model for control and learning of voluntary movement
Kawato, M., Furukawa, K., & Suzuki, R. (1987) · Biological Cybernetics · doi:10.1007/bf00364149 · Cited by 1,573
Resolved-acceleration control of mechanical manipulators
Luh, J., Walker, M., & Paul, R. (1980) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1980.1102367 · Cited by 1,203
Cited by
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1805.12114 · Cited by 525
Neural network control for a closed-loop System using Feedback-error-learning
Gomi, H., & Kawato, M. (1993) · Neural Networks · doi:10.1016/s0893-6080(09)80004-x · Cited by 288
Metadata
- Authors
- Miller, W. T., Hewes, R. P., Glanz, F. H., & Kraft, L. G.
- Venue
- IEEE Transactions on Robotics and Automation
- Published
- 1990
- Volume
- 6
- Issue
- 1
- Pages
- 1-9
- ISSN
- 1042-296X (ISSN-L)
- OpenAlex
- W1964809733
- Cited by
- 318
- References
- 28
- FWCI
- 19.78
- Type
- journal article
- Language
- en
- Open access
- closed
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