원문정보
초록
영어
To improve the accuracy and reduce the response time of traditional gas regulator, a Proportional Integral Derivative (PID) neural network controller of electric gas regulator is proposed by combining conventional digital PID with neural network. And the parallel architecture of the PID neural network is implemented by using Field Programmable Gate Array (FPGA). The neural network is demonstrated in a closed loop system of electric gas regulator and the ideal system output can be obtained by using this improved neural network algorithm. Theoretical analysis and simulation results show that the PID neural network pressure controller based on FPGA can achieve faster response speed and higher control accuracy compared with those based on software, and be obvious to improve the efficiency and security of the electric gas regulator.
목차
1. Introduction
2. The Control System Model of Electric Gas Regulator
2.1. The Mathematical Model of Driving Motor
2.2. The Control System Model of Electric Gas Regulator
3. The PID Controller based on the Neural Network
3.1. The Back Propagation Algorithm of PID Neural Network
3.2. The Structure of PID Control System based on the BP Neural Network
4. The Realization of PID Neural Network based on FPGA
5. Analysis and Comparison of Simulation
6. Conclusion
References