An adaptive neurocontroller using RBFN for robot manipulators
Min-Jung Lee; Young-Kiu Choi
Industrial Electronics, IEEE Transactions on
Volume 51, Issue 3, June 2004 Page(s): 711 - 717
Digital Object Identifier   10.1109/TIE.2004.824878
Summary: 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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