Application of Fuzzy Neural Network PID Controller in Networked Control Systems

2013 ◽  
Vol 273 ◽  
pp. 689-693
Author(s):  
Zi Yi Fu ◽  
Lu Wang ◽  
Lei Wang
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Networked Control Systems ◽  
Control Object ◽  
Networked Control ◽  
Control Performance ◽  
Fuzzy Neural ◽  
Neural Network Pid

Aiming at the performance degradation and system destabilization which are caused by time delay in networked control systems (NCSs), a novel fuzzy neural network PID controller is proposed to alleviate the adverse effect. This approach enjoys the advantage of functional mapping of the fuzzy neural network, and gives better performance in tuning the PID controller parameters online. The simulation of the improved networked controller is carried out through the matlab/truetime, and the DC motor which has higher real-time performance is chosen as a control object. The simulation results illustrate that the controller can effectively improve the control performance and keep the system stable.

Design of Networked Control Systems Based on Single-Neuron PID

2011 ◽  
Vol 219-220 ◽  
pp. 1101-1104
Author(s):  
Yong Xian Jin ◽  
Gao Feng Che ◽  
Yi Sheng Guan
Keyword(s):  
Control Systems ◽  
Pid Controller ◽  
Single Neuron ◽  
Networked Control Systems ◽  
Design Method ◽  
Networked Control ◽  
Control Performance ◽  
Performance Requirements ◽  
Single Neuron Pid ◽  
Delay Control Systems

With the development of control theory and communication technology, networked control systems have got more and more attention. The traditional PID controller is no longer suitable to meet performance requirements of time-delay control systems. And communication network is introduced into control systems that makes systems arise some problems, such as time delays, data dropout and so on. These problems affect the control performance badly. A model based on the single-neuron PID controller for networked control systems is proposed to improve the control performance. The design method for controller is based on neural network, traditional PID controller and Smith predictive control. The simulation results represent the model we proposed has better control performance.

Research of Genetic Algorithm Controller Based Fuzzy Neural Network in Vacuum Pre-Cooling Freshness-Keeping Machine

2014 ◽  
Vol 926-930 ◽  
pp. 3545-3549
Author(s):  
Ke Liang Zhou ◽  
Qiong Tan ◽  
Jian He
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Fuzzy Controller ◽  
Control Object ◽  
Bp Algorithm ◽  
Control Effect ◽  
Fuzzy Neural ◽  
The Difference

The control object is the temperature of pre-cooling machine, combined the advantage of neural network and genetic algorithm (GA). Adopting GA controller based fuzzy neural network. The controller doing the fuzzy reasoning to the difference of given temperature and sample temperature. GA does the offline training to the Connection weights and Membership function of fuzzy neural network, then uses BP algorithm to do further adjust online for parameters. Simulation result shows that the new controller achieves better control effect compared with traditional PID controller, fuzzy controller.

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