Neuron PID Control for a BPMSM Based on RBF Neural Network On-Line Identification

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

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Study on RBF Neural Network PID Control for Hydro-Viscous Drive System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.470.668 ◽

2013 ◽

Vol 470 ◽

pp. 668-672

Author(s):

Qing Rui Meng ◽

Kai Wang ◽

Dao Ming Wang ◽

Jian Wang ◽

Bao Cheng Song ◽

...

Keyword(s):

Neural Network ◽

Error Rate ◽

Control Strategy ◽

Pid Control ◽

Rbf Neural Network ◽

Drive System ◽

Output Curve ◽

Strategy Selection ◽

Neural Network Pid ◽

Selection Of

To verify the applicability of RBF neural network PID control on speed regulating start control for hydro-viscous drive system, analyze the principle of RBF neural network PID control, the simulation model is established based on SIMULINK and the control characteristics are analyzed based on the AMESim/MATLAB co-simulation. The results show that RBF neural network PID control has a good self-correcting effect on speed regulating start of hydro-viscous; it can make right judgments according to the error and error rate and adjust the output speed towards opposite direction of error; meanwhile, it ensures the smoothness of output curve and avoids excessive mechanical impact. The results play a guiding role for control strategy selection of speed regulating start.

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On-Line Thickness Measurement of Thin Film Based on Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.484-485.307 ◽

2014 ◽

Vol 484-485 ◽

pp. 307-310

Author(s):

Li Cai ◽

Yue Gang Tan ◽

Qin Wei

Keyword(s):

Neural Network ◽

Thin Film ◽

Film Thickness ◽

Production Cost ◽

Rbf Neural Network ◽

Thickness Measurement ◽

Monitoring Method ◽

Line Thickness ◽

On Line ◽

Thickness Deviation

This paper proposes a on-line thickness measurement scheme of thin film based on the capacitance thickness sensor and introduces the composition and principle of thickness measurement system. Then it further states the principle and simulation of the RBF neural network, which can effectively predict the thickness deviation of thin film by setting the appropriate parameters. The monitoring method based on the RBF neural network will reduce production cost and make the film thickness uniformity better, combining the traditional film production line with an new idea of controlling the opening degree of wind ring.

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