The Design of the Resistance-Heated Furnace Control Based on BP Network PID Algorithm

Abstract:

The resistance-heated furnace control system has bigger lagging nature, so it is difficult to build accurate mathematic model BP neural network has the capability of expression nonlinearity and also has the self study and adaptive function. BP neural network control makes full use of neural network approximation capability, and with better control in resolving the highly nonlinear seriously uncertain systems. Simulated result indicates this control is able to make system reach satisfied control effect, so it has fairly good application value.

• Periodical: Advanced Materials Research (Volume 214)
• Pages: 281-285
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.214.281
• Online since: February 2011
• Authors: Huai Zhong Chen
• Keywords: BP Neural Network (BPNN), PID Parameter Tuning, Temperature Control

References

[1] SUN Xiao-quan, QIAN Shao-ming: PID controller of barrel temperature based on BP neural network, Mechanical&Electrical Engineering, Vol. 25, No. 5(2008), pp.18-21.

[2] Sun Ｑiuping, Li Wenfeng: Study of Constant Pressure Water supplying System Based on PID Algorithm of BP Network, Electrical Automation，Vol. 30, No. 5 (2008), pp.12-14.

[3] ZHANG Xueyan, GAO Peijin: The Researching And Simulation of BP Neural Network PID Controller in Industry Control System. Techniques of Automation&Applications, Vol. 29, No. 5(2010), pp.9-12.

[4] Liu, Jinkun: Advanced PID control algorithms and mat lab simulation, Beijing Publishing House of Electronics Industry (2004).

[5] Hao Lina: Research on Simulation of Self-tuning PID Controller based on BP Neural Network, Computer Development & Applications, Vol. 21, No. 3(2008) , pp.9-11.