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Adaptive neural network control of robot manipulators in task space
Abstract
In this paper, the adaptive neural network control of robot manipulators in the task space is considered. The controller is developed based on a neural network modeling technique which neither requires the evaluation of inverse dynamical model nor the time-consuming training process. It is shown that, if Gaussian radial basis function networks are used, uniformly stable adaptation is assured and asymptotically tracking is achieved. The controller thus obtained does not require the inverse of the Jacobian matrix. In addition, robust control can be easily incorporated to suppress the neural network modeling errors and the bounded disturbances. Numerical simulations are provided to show the effectiveness of the approach.
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Cited by
FPGA Design Methodology for Industrial Control Systems—A Review
Monmasson, E., & Cirstea, M. N. (2007) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.898281 · Cited by 908
RISE-Based Asymptotic Prescribed Performance Tracking Control of Nonlinear Servo Mechanisms
Wang, S., Na, J., & Ren, X. (2018) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2017.2769683 · Cited by 159
Metadata
- Authors
- Ge, S. S., Hang, C. C., & Woon, L. C.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 1997
- Volume
- 44
- Issue
- 6
- Pages
- 746-752
- ISSN
- 0278-0046 (ISSN-L)
- OpenAlex
- W2151566726
- Cited by
- 189
- References
- 0
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ge, S. S., Hang, C. C., & Woon, L. C. (1997). Adaptive neural network control of robot manipulators in task space. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/41.649934
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