论文信息 - Neural network robot controller based on structural learning with forgetting

Neural network robot controller based on structural learning with forgetting

In this paper, a neural network based controller is proposed for robot manipulators. By considering the second order term of the Taylor expansion of the robot dynamics, the weight tuning algorithm can guarantee the tracking performance of the robot with unknown dynamics. The generic structure selection problem for the neural network controller is addressed by using the structural learning with forgetting, which can automatically remove the redundancy in the structure. Simulations have been conducted on trajectory tracking for various elliptic trajectories. The result demonstrates the effectiveness of the proposed controller.

Simon X. Yang | M. Ishikawa | Xiang Yu

[1] M. Ishikawa. Structural learning and its applications to rule extraction , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[2] Ke Chen,et al. A self-generating modular neural network architecture for supervised learning , 1997, Neurocomputing.

[3] Max Q.-H. Meng,et al. Neural network approaches to dynamic collision-free trajectory generation , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[4] Frank L. Lewis,et al. Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[5] Masumi Ishikawa,et al. Structural learning with forgetting , 1996, Neural Networks.

[6] Ke Chen,et al. Combining linear discriminant functions with neural networks for supervised learning , 2005, Neural Computing & Applications.

[7] Frank L. Lewis,et al. Intelligent optimal control of robotic manipulators using neural networks , 2000, Autom..

[8] Frank L. Lewis,et al. Neural net robot controller with guaranteed tracking performance , 1995, IEEE Trans. Neural Networks.