An adaptive neurocontroller using RBFN for robot manipulators
Min-Jung Lee
Young-Kiu Choi
Dept. of Electron. & Inf., Kyungnam Coll. of Inf. & Technol., Pusan, South Korea;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: June 2004
Volume: 51,
Issue: 3
On page(s): 711- 717
ISSN: 0278-0046
INSPEC Accession Number: 8110765
Digital Object Identifier: 10.1109/TIE.2004.824878
Current Version Published: 2004-06-01
Abstract
In recent years, neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neurocontroller for robot manipulators based on the radial basis function network (RBFN). The RBFN is a branch of neural networks and is mathematically tractable. Therefore, we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. A saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.
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