PID neural network control of a membrane structure inflation system

Abstract

Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

Keywords

PID / neural network / membrane structure

References

[1] Xiao Z, Chen Y, Shu H. Study on PID neural networks for complicated temperature system. Microcomputer Information, 2009, 25(1–1): 63–64

[2] Zhu L. Research on the intelligent temperature control system based on improved neuron PID algorithm. Microcomputer Information (embedded and SOC), 2010, 26(4–2): 56–58

[3] Shu H L. PID Neural Network and Its Control System. Beijing: National Defence Industry Press, 2006 (in Chinese)

[4] Tao Y H. New PID Control and Its Application. 2nd edition. Beijing: China Machine Press, 2003, 34–60 (in Chinese)

[5] Liu J K. Advanced PID Control and MATLAB Simulation. Beijing: Electronic Industry Press, 2003 (in Chinese)

[6] Feng D B, Ma S L, Chen T J. Parameter feedback fuzzy controller analysis and design of time-varying nonlinear system. Information and Control, 2002, 31(4): 310–314 (in Chinese)

[7] Yu B. One type of self-correction fuzzy control of time-varying and delay system. Journal of North China Electric Power University, 2002, 22(2): 14–17

Acknowledgments

This work was sponsored by the Key Laboratory of Modern Measurement & Control Technology (Beijing Information Science & Technology University), Ministry of Education and National Natural Science Foundation of China (Grant No. 50975020), Key Project of Science and Technique Development Plan Supported by Beijing Municipal Commission of Education(KZ200910772001), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipalipality (PHR20090518), and Open Project Supported by Beijing Key Laboratory on Measurement and Control of Mechanical and Electrical System (KF20091123206, KF2009112302).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg