AN ULTRASONIC MOTOR SPEED CONTROL SCHEME BASED ON NEURAL NETWORKS AND ITERATIVE CONTROLLER

2004 ◽  
Vol 01 (03) ◽  
pp. 457-470
Author(s):  
X. H. SHI ◽  
Y. C. LIANG ◽  
X. XU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Speed Control ◽  
Ultrasonic Motor ◽  
Random Disturbance ◽  
Motor Speed ◽  
Neural Network Controller ◽  
Learning Rates ◽  
Network Controller ◽  
Control Scheme

An ultrasonic motor speed control scheme is presented in this paper based on neural networks and iterative controller. Suitable ranges of the adaptive learning rates of neural network controller are presented through the theoretical analysis on the proposed model, which could guarantee its stability. The convergence of iterative controller is also discussed. Numerical results show that the control scheme is effective for various kinds of reference speeds of ultrasonic motors. Comparisons with the existing method show that the precision of control could be increased using the proposed method. Simulations also show that the proposed scheme is fairly robust against random disturbance to the control variables.

scholarly journals A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaohu Li ◽  
Feng Xu ◽  
Jinhua Zhang ◽  
Sunan Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Small World ◽  
Random Disturbance ◽  
Proportional Integral ◽  
Neural Network Controller ◽  
Actuation System ◽  
Network Controller

Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance.

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