Integral Separation PID Control of Certain Electro-hydraulic Servo System based on RBF Neural Network Supervision

DOI

10.2991/amcce-17.2017.7How to use a DOI?

Keywords

Electro-hydraulic Servo System; Nonlinear; RBF Neural Network Supervision RBFNNS); Integral Separation PID Control (ISPID)

Abstract

Electro-hydraulic servo control system is a nonlinear and uncertain system with time-variation parameters and external disturbance. The traditional Proportion Integration Differentiation(PID) controller can hardly control the nonlinear and time-variant systems, whereas the Radial Basis Function (RBF) controller may solve the problem under the condition of the control parameters' reasonable selection. In order to ensure the static and dynamic performance of certain electro-hydraulic servo system, an Integral Separation PID Controller (ISPID) based on RBF Neural Network Supervision RBFNNS was proposed. Taking advantage of the ISPID Controller and RBFNNS, optimum control of certain electro-hydraulic servo system was achieved The results of MATLAB simulation and the prototype tests show that the overshoot of the proposed system is 2.5%, the steady state error of step response is about 0.10 0.91mm and the error of prototype depth control is no more than ±10mm. Compared with the traditional PID controller and the RBF controller, the proposed system has advantages of speedy response, smaller overshoot , high steady-state accuracy and strong robustness. It proves that the proposed control scheme is effective and suitable

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - RongLin Wang
AU  - BaocHun Lu
AU  - Wenbin Ni
PY  - 2017/03
DA  - 2017/03
TI  - Integral Separation PID Control of Certain Electro-hydraulic Servo System based on RBF Neural Network Supervision
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 37
EP  - 42
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.7
DO  - 10.2991/amcce-17.2017.7
ID  - Wang2017/03
ER  -