Application of Multivariable Non-Linear Decoupling Control Based on Neural Network


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Aimed at Neural Network can approach any nonlinear system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. In this paper, we designed and finished the Distributed Control System based on ABB’s Freelance 800F, and collected experimental data to model the thermostatic heater, then we have carried out the mathematical model by means of MATLAB dynamic simulation. In sequence, we trained the neural network controller in MATLAB. When the decoupling is completed, we used controller to control the MIMO nonlinear system in DCS. Experiment result shows that it is conscientiously feasible and deserves to be widely applied in the process of controlling industry.



Edited by:

Shaobo Zhong and Zhigang Liu






J. B. Zhang et al., "Application of Multivariable Non-Linear Decoupling Control Based on Neural Network", Applied Mechanics and Materials, Vol. 214, pp. 786-791, 2012

Online since:

November 2012




[1] D. He, X. ZH. Dai. (2000) Novel Method for Decoupling MIMO Nonlinear System with Linearization (II)-Discrete-time System. Control and Design 14(6): 631-635.

[2] YOUN H. TING (1972) Self-Regulating PTC Heating Systems: A New Approach for Electric Heating Appliances. INDUSTRY APPLICATIONS 8(3): 338-344.

DOI: 10.1109/tia.1972.349765

[3] HOWARD BECKER, HELMUT OHNMACHT. (1985) PTC Heating Elements-Background Analysis and Design Attributes. Industry Applications 21(4): 896-898.

DOI: 10.1109/tia.1985.349538

[4] YANG Peng, LIU Pin-jie, ZHANG Yan, LI Yong-fu. (2009) Improved Multivariable Predictive Control Based on RBF Networks. Control Engineering of China. 16(1): 39-41, 55.

[5] HUANG Jun-an, WANG Yue, LI Xiu-zhen. (2009) Neural network decoupling available to a multivariable control system. Industrial Instrumentation & Automation 11: 11-14.

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