The Optimization for PID Controller Parameters Based on Genetic Algorithm

Xiang Jie Niu

doi:10.4028/www.scientific.net/AMM.513-517.4102

Paper Titles

Prediction for Burned Area of Forest Fires Based on SVM Model
p.4084

Numerical Simulation of Black-Scholes Model by Finite Difference Method
p.4090

Controlling of the Pollution Based on a Prediction Model
p.4094

Dynamics Analysis and Control Strategy Simulation of Lower Extremity of Power Assist Robot
p.4098

The Optimization for PID Controller Parameters Based on Genetic Algorithm
p.4102

Study on Nonlinear Problem of Active Noise Control System for Power Transformers
p.4106

Automatic Control System Fault Image Recognition Technology
p.4111

Optimization Control for Logistics and Transport with Constraints in Returns
p.4115

The Application of Computer Vision in Cultural Production Innovation
p.4119

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517The Optimization for PID Controller Parameters...

The Optimization for PID Controller Parameters Based on Genetic Algorithm

Abstract:

as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID parameters as individuals in genetic algorithm during the control process, and takes the integral function of absolute error control time as the optimization object to dynamically adjust the three PID control parameters, thus realize online optimization for PID control parameters. Simulation results show that the introduction of genetic algorithms for PID control system improves the dynamic performance, enhance system stability and operation speed, and get better control effect.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

4102-4105

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.4102

Citation:

Cite this paper

Online since:

February 2014

Authors:

Xiang Jie Niu*

Keywords:

Genetic Algorithm (GA), Optimization, Parameter Tuning, Proportional Integral Microcontroller

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Abido, M. A. Particle swarm optimization formultimachine power system stabilizer design[J]. Power Engineering Society Summer Meeting, 2001, (3): 1346-1349.

DOI: 10.1109/pess.2001.970272

Google Scholar

[2] Liu Ke, Wan Li Wu Jieming. Based on data mining technology of network security event prediction research [J]. BULLETIN OF SCIENCE AND TECHNOLOGY , 2012, 28 (6) : 37 to 40.

Google Scholar

[3] Wang Q G, Fung H W, Lee T H. PID Tuning for Improved Performance[J]. IEEE Trans Control System Tech, 1999, 7(4): 457-465.

Google Scholar

[4] Liu Di Zhao Jianhua. A BP neural network model based on the adaptive PID control algorithm [J]. Automation technology and applications, 2008, 7 (8) : 8-10.

Google Scholar

[5] Zhao Ruijun Wang Xianlai. Fuzzy - PID controller in the application of air conditioning temperature control [J]. Computer simulation, 2006, 23 (11) : 311-313.

Google Scholar