<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A novel intelligent neural network control scheme which integrates the merits of fuzzy inference, neural network adaptivity and simple PID method is presented in this paper. This control method overcomes the defects existed in the traditional variable frequency induction motor driven hydraulic source, such as slow response, poor control precision, easy to overshoot. Permanent magnet synchronous motor driven constant pump hydraulic system is designed instead of common motor, energy saving, fast response and easy to realize closed loop control. System uses the structure of the combination of neural network control and RBF network online identification. The parameters of the controller are optimized by PSO algorithm offline and error back propagation (BP) algorithm offline, and a RBF network is built to identify the system online. The hydraulic power system’s control simulation experiments are conducted, and the experimental results at the typical working conditions of the hydraulic source show that the controller and its optimization algorithm can effectively improve the system performance, and the system has no steady-state error, good dynamic performance and good robustness, superior to conventional fuzzy controller and PID controller.</p></abstract>

A novel intelligent neural network control scheme which integrates the merits of fuzzy inference, neural network adaptivity and simple PID method is presented in this paper. This control method overcomes the defects existed in the traditional variable frequency induction motor driven hydraulic source, such as slow response, poor control precision, easy to overshoot. Permanent magnet synchronous motor driven constant pump hydraulic system is designed instead of common motor, energy saving, fast response and easy to realize closed loop control. System uses the structure of the combination of neural network control and RBF network online identification. The parameters of the controller are optimized by PSO algorithm offline and error back propagation (BP) algorithm offline, and a RBF network is built to identify the system online. The hydraulic power system’s control simulation experiments are conducted, and the experimental results at the typical working conditions of the hydraulic source show that the controller and its optimization algorithm can effectively improve the system performance, and the system has no steady-state error, good dynamic performance and good robustness, superior to conventional fuzzy controller and PID controller.

Intelligent Neural Network Control Strategy of Hydraulic System Driven by Servo Motor

Xi’an University of Architecture and Technology

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A novel intelligent neural network control scheme which integrates the merits of fuzzy inference, neural network adaptivity and simple PID method is presented...

Intelligent Neural Network Control Strategy of Hydraulic System Driven by Servo Motor

Data publikacji: 01 cze 2015

Zakres stron: 1406 - 1423

Otrzymano: 21 lut 2015

Przyjęty: 05 maj 2015

DOI: https://doi.org/10.21307/ijssis-2017-812

© 2015 Ma Yu published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.