JOURNAL ARTICLE

Robust Neural Network Controller Design For A Biaxial Servo System

Chih‐Hsien YuTien‐Chi Chen

Year: 2007 Journal:   Asian Journal of Control Vol: 9 (4)Pages: 390-401   Publisher: Wiley

Abstract

ABSTRACT A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new neural network based cross‐coupled control and neural network techniques are used together to cancel out the skew error. In the proposed control scheme, the conventional fixed gain PID cross‐coupled controller (PIDCCC) is replaced with the neural network cross‐coupled controller (NNCCC) to maintain biaxial servo system synchronization motion. In addition, neural network PID position velocity and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. This scheme provides strong robustness with respect to uncertain dynamics and nonlinearities. The simulation results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Keywords:
Control theory (sociology) Synchronizing Artificial neural network Robustness (evolution) PID controller Servomechanism Control engineering Synchronization (alternating current) Computer science Motion control Robust control Servo Control system Engineering Control (management) Robot Artificial intelligence Temperature control

Metrics

11
Cited By
0.00
FWCI (Field Weighted Citation Impact)
12
Refs
0.07
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Iterative Learning Control Systems
Physical Sciences →  Engineering →  Control and Systems Engineering
Adaptive Control of Nonlinear Systems
Physical Sciences →  Engineering →  Control and Systems Engineering
Chaos control and synchronization
Physical Sciences →  Physics and Astronomy →  Statistical and Nonlinear Physics

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