Optimization Of The Control Parameters Of A Servosystem Based On The Genetic Algorithm Method

DOI

10.2991/jimec-17.2017.115How to use a DOI?

Keywords

Control parameter optimization; the genetic algorithm method; servosystem control; PID control algorithm.

Abstract

The control parameters optimization of the servosystem in parallel robots, which is the basis of the control system design of the robot, is very important. In this paper, the parameters optimization of the servosystem based on the genetic algorithm method is introduced. In the servosystem of parallel robots, a three-closed-loop PID control method is usually used. So the control parameters of the speed control loop and position control loop of the servosystem have great influence on the performance of the motor. To meet the requirement of the control system, a three-closed-loop PID control structure of the servosystem is designed according to the hardware system of the AC servo. With the time-domain performance and dynamic characteristic as the optimization targets, the control parameters of the speed control loop and position control loop are optimized. Comparing with the tuning result optimized by the traditional Ziegler-Nichols tuning laws, the results obtained by the GA method show better performance in the computer simulation of a parallel robot. Therefore, the GA method which is used in the optimization of the control parameters of the servosystem, has the advantages of simple calculation, fast tuning speed, good optimization result, and so on.

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  - Qi Hao
AU  - Zhankui Qiu
AU  - Xiaowei Wang
AU  - Antong Gao
PY  - 2017/10
DA  - 2017/10
TI  - Optimization Of The Control Parameters Of A Servosystem Based On The Genetic Algorithm Method
BT  - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017)
PB  - Atlantis Press
SP  - 534
EP  - 537
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimec-17.2017.115
DO  - 10.2991/jimec-17.2017.115
ID  - Hao2017/10
ER  -