Study on Multivariable System Based on PID Neural Network Control

Abstract:

The multivariable PID neural network (MPIDNN) control system is introduced in this paper. MPIDNN is used to perform both the control and the decouple at the same time and to get better performance. It is difficult to control multivariable system by conventional controller because the strong coupling properties of the system. Generally, the decoupling system should be designed first and the multivariable object would be divided into several single variable objects. Then, several simple controller would achieve the control of those objects. The decoupling system and the controller exist in theory but the design process is very difficult actually because the transfer function of the object is difficult to get. Especially, if the number of the object inputs is not equal to that of the object outputs, which is called unsymmetry object, the conventional decoupling is impossible. A actual example is discussed in the paper in order to prove the function of the MPIDNN, in which an un-symmetry multivariable system which has 3 inputs and 2 outputs is controlled by a MPIDNN and the perfect control property is obtained by self-learning process.

• Periodical: Advanced Materials Research (Volumes 591-593)
• Pages: 1490-1495
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.591-593.1490
• Online since: November 2012
• Authors: Huai Lin Shu, Jin Tian Hu
• Keywords: Coordination Control, Decoupling Control, Multivariable System, PID Neural Network

References

[1] H.L. Shu.PID Neural Network Control System Analysis: ACTA AUTOMATICA SINICA,Vol.25(1999),pp.105-111(In Chinese)

[2] H.L Shu.PID Control Combine with Neural Network and PID Neural Network Nonlinear System: Proceedings of The 19th Chinese Control Conference (CCCC 2000). Dec.6-8, 2000, Hong Kong,China;626-630

DOI: 10.1109/chicc.2006.280578

[3] H.L Shu. PID neural network control for complexsystems . Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation(CIMCA99'), IOS Press. 1999,2;166-171

[4] L. Wang M.R. Chen Y.K. Sun and S.W. Feng. Cold Rolling Plate Type Thickness Based on PID Neural Network Multivariable Control: Metallurgical Industry Automation, 2002,2: 15-18(In Chinese)

[5] J. Wang,W. Tong,L. Shi and Q.C. Ren. PID Neural Network Decoupling Control Technology in The VAV System: Journal of Northwest University(JCR-SCI), 2002,32(3): 237-239(In Chinese)