Intelligent Control of Mobile Agent Based on Fuzzy Neural Network in Intelligent Robotic Space

Author(s):  
TaeSeok Jin ◽  
HongChul Kim ◽  
JangMyung Lee
2014 ◽  
Vol 644-650 ◽  
pp. 341-345
Author(s):  
Ming Jun Li ◽  
Hua Tian ◽  
Xiao Jing Guo

In this paper, intelligent fuzzy control theory is introduced in the model of neural network algorithm, and the neural network system is improved by the PID controller, which has realized the feedback and adjustment function of neural network system, and has made the reaction of the system be more accurate and stable. In order to verify the validity and reliability of the designed intelligent control PID algorithm based on the fuzzy neural network in this paper, the algorithm is carried on the programming by using Matlab programming software, and the control process of PID is calculated by NNbox simulation toolbox, at last, it has obtained the curve of PID control response changing over time. From the response curve, it can be seen that after the PID proportional coefficient is regulated by using fuzzy neural network intelligent control algorithm, it can quickly and steadily obtain the control curve, which has realized better intelligent control effect, and has provided technical reference for the research of intelligent PID controller.


2011 ◽  
Vol 128-129 ◽  
pp. 168-171
Author(s):  
Gang Li ◽  
Hao He ◽  
Gang Fang ◽  
Jian Feng Wu

Intelligent control methods of missile guidance and control system (GACS) are studied in this paper. Secondly, the component and principle of GACS is introduced. Based on the fuzzy neural network, this paper constructs a basic structure of the intelligent control method of missile. Meanwhile, a new intelligent control method of rolling channel of missile based on Fuzzy Cerebella Model Articulation Controller (FCMAC) is designed. Under complicated environmental conditions, the missile can be accurately controlled with this method. Finally, the application value is illustrated. It’s very meaningful to improve the combat capability.


2014 ◽  
Vol 1044-1045 ◽  
pp. 712-715
Author(s):  
Qin Hui Gong

In view of biomass gasify process which has nonlinear, non-minimum- phase, big delay and strong load interference characteristics, a fuzzy neural network control algorithm for biomass gasifier is presented. Gasified process of biomass gasifier has been researched, and intelligent control of temperature and primary air flow for biomass gasifier has been designed. The control objects are gasifier temperature and flue gas oxygen content , the regulation objects are the material feed flow and primary air flow. Simulation results show that, this system has better control effect than the traditional fuzzy control system.


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