Details

Autor(en) / Beteiligte

• Tsai, Ching-Chih
• Lin, Shui-Chun
• Cheng, Tai-Shen
• Chan, Cheng-Kai

Titel

Adaptive integral position control using RBF neural networks for brushless DC linear motor drive

Ist Teil von

• 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006, p.3188-3193

Ort / Verlag

IEEE

Erscheinungsjahr

2006

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The paper presents an adaptive integral position controller usingRBF (Radial Basis Function) neural networks (NNs) for a brushless DC linear motor. By assuming that the upper bounds of the nonlinear friction and force ripple, an RBF NN is used for approximating the friction, the force ripple and the load; an adaptive backstepping control with integral action is then proposed to achieve position tracking of the linear motor. The parameter adjustment rules for the overall controller are derived via the Lyapunov stability theory. Based on the LaSalle-Yoshizawa lemma, the proposed controller is proven asymptotically stable. Experimental results are conducted to show the efficacy and usefulness of the proposed control method.