Design Neural Network Controller for Mechatronic System

Authors: Ismail Algelli Sassi Ehtiwesh, Mohamed Ali Elhaj

Abstract:

The main goal of the study is to analyze all relevant properties of the electro hydraulic systems and based on that to make a proper choice of the neural network control strategy that may be used for the control of the mechatronic system. A combination of electronic and hydraulic systems is widely used since it combines the advantages of both. Hydraulic systems are widely spread because of their properties as accuracy, flexibility, high horsepower-to-weight ratio, fast starting, stopping and reversal with smoothness and precision, and simplicity of operations. On the other hand, the modern control of hydraulic systems is based on control of the circuit fed to the inductive solenoid that controls the position of the hydraulic valve. Since this circuit may be easily handled by PWM (Pulse Width Modulation) signal with a proper frequency, the combination of electrical and hydraulic systems became very fruitful and usable in specific areas as airplane and military industry. The study shows and discusses the experimental results obtained by the control strategy of neural network control using MATLAB and SIMULINK [1]. Finally, the special attention was paid to the possibility of neuro-controller design and its application to control of electro-hydraulic systems and to make comparative with other kinds of control.

Keywords: Neural-Network controller, Mechatronic, electrohydraulic

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074765

References:

[1] K. Singh and G. Agnibori. System Design Through Matlab, Control toolbox and Simulink. Springer, 2000.
[2] Jelali and Kroll. Hydraulic Servo-systems, Modeling, Identification and Control. Springer, UK, 2002.
[3] H. E. Merritt. Hydraulic Control Systems. John Wiley&Sonns, USA, 1967.
[4] K Ogata. Modern Control Engineering. Aeeizh, USA, 2002.
[5] D. Pham and L. Xing. Neural Networks for Identification, Prediction and control. Springer, 1997.
[6] Simon Haykin. Neural Networks. Macmillan, USA, 1994.
[7] K. Astrom and B. Wittenmark. Computer controlled Systems. Prentice- Hall, 1991.
[8] P. Wasserman. Neural computing Theory and Practice. Van Nostrand Reinhold, New York 1991.