Neural network controller for flexible plate considering spillover effect on learning process

We propose noncontact sensing and control methods of flexible plate materials handled by a robotic manipulator. A displacement sensor and a force/torque sensor are introduced to measure vibration. Based on the proposed neural network controller, which employs the feedback error learning method as the basic control architecture, this paper deals with the spillover effect to asymptotic convergence of learning. The conditions for asymptotic convergence of feedback error learning method for each trials are obtained. The influence of the vibration modes unmodeled on the conditions for asymptotic convergence is discussed. Based on the results obtained here, we present a new learning method to improve the control performance. The control system consists of a low pass filter and two neural networks. The learning method is to change the learning rate according to the convergence conditions. From simulation results, we show that the tracking performance is improved by using proposed learning method.

[1]  Masayoshi Wada,et al.  Learning control of elastic joint robot and its application to the industrial robot manipulator , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[2]  Fumihito Arai,et al.  Asymptotic convergence of feedback error learning method and improvement of learning speed , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[3]  Fumihito Arai,et al.  Trajectory control of flexible plate using neural network , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[4]  Fumihito Arai,et al.  Asymptotic convergence of feedback error learning method considering spillover in controlling flexible structure , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[5]  Shigeru Okuma,et al.  Trajectory Control of Manipulators Based on Discrete Time Learning Control with 2-Delay Input Control Technique , 1994 .

[6]  Yuan F. Zheng,et al.  Vibration-free movement of deformable beams by robot manipulator , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[7]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[8]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[9]  Fumihito Arai,et al.  Recurrent neural network modeling and learning control of flexible plates by nonlinear handling system , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[10]  S. Jain,et al.  Vibration suppression of unknown flexible payloads using a wrist mounted force/torque sensor for remote manipulator systems , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[11]  Akira Sano,et al.  Adaptive Control Incorporating Neural Network , 1994 .

[12]  M. Balas.  Active control of flexible systems , 1978 .

[13]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.