Force Control of Electrical Load System Based on Single Neuron PID Adaptive and Repetitive Control

DOI

10.2991/iccsee.2013.303How to use a DOI?

Keywords

surplus torque, electrical load system, single neuron, PID control, repetitive control

Abstract

In view of the surplus torque and complexity of controlled plant in passive electrical load system, a novel approach based on single neuron PID adaptive control and repetitive control for repetitive periodic load control system is proposed. Radial basis function (RBF) neural network is used to identify the system on-line for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting based on the desired output. The dynamic state performance can be improved by the single neuron adaptive PID control and the steady state performance is also improved by modified repetitive control. Computer simulation results show that the force/position hybrid control system can effectively reduce the surplus torque and improve the loading precision, and also it has fine dynamic and steady state performance and good robustness. The reliability of whole system is further improved.

Copyright

© 2013, 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  - Zhiqiang Wei
AU  - Guanghua Zong
AU  - Hongchong Wu
PY  - 2013/03
DA  - 2013/03
TI  - Force Control of Electrical Load System Based on Single Neuron PID Adaptive and Repetitive Control
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1205
EP  - 1209
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.303
DO  - 10.2991/iccsee.2013.303
ID  - Wei2013/03
ER  -