Title :
Adaptive output recurrent neural network for overhead crane system
Author :
Chiu, Chih-Hui ; Lin, Chun-Hsien
Author_Institution :
Dept. of Electr. Eng., Yuan-Ze Univ., Taoyuan, Taiwan
Abstract :
In this study, an adaptive output recurrent neural network (AORNN) controller is employed to control a practical overhead crane system with multi objective control problems. Trolley position error and swing angle error are used instead of a complex dynamic model to design the controller. The gradient descent method is adopted to adjust the AORNN parameters online. Moreover, an analytical method based on a Lyapunov function is proposed to determine the learning rates of the AORNN so that the convergence of the system can be guaranteed. Finally, the effectiveness of the proposed control system is verified by experiment and simulation of overhead crane system. The results show that AORNN control system can have a good performance in application.
Keywords :
Lyapunov methods; control system synthesis; cranes; gradient methods; neurocontrollers; recurrent neural nets; trolleys; Lyapunov function; controller design; gradient descent method; multiobjective control problems; output recurrent neural network controller; overhead crane system; swing angle error; trolley position error; Adaptation model; Artificial neural networks; Control systems; Convergence; Cranes; Mathematical model; Recurrent neural networks; Lyapunov; gradient descent method; neural network; overhead crane;
Conference_Titel :
SICE Annual Conference 2010, Proceedings of
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7642-8
