The Research of Genetic Algorithms and Fuzzy Neural Network Adaptive Control Applicated in the Shuttle Kiln Control
|School||Jingdezhen Ceramic Institute|
|Course||Energy and Power Engineering|
|Keywords||Shuttle kiln Genetic Algorithms Fuzzy Neural Network VC 6.0 MATLAB|
This topic is divided into two parts, the first part of the genetic algorithm to optimize the gas shuttle kiln heating rate . The various stages of ceramic firing process requirements as a genetic algorithm to optimize the shuttle kiln heating rate constraints. And the heating rate of the various stages of real coding , the total gas consumption as an indicator of fitness function for genetic manipulation . The results showed that the optimized heating rate in the shuttle kiln firing can take to save fuel consumption effect : Part II based model reference adaptive control (MRAC) on the gas shuttle kiln temperature control. Since shuttle kiln as a control object exists in the process of non-linear , long lag, when the variability and uncertainty of the situation , the general conventional PID control can not meet the control precision. Therefore, this paper proposes MRAC control scheme , which uses RBF RBF network as a shuttle kiln temperature object recognition network , it produces Jacobin information provided together with the control error fuzzy neural network controller as their learning signal. After adjustment of the initial parameters MATLAB simulation , simulation results show that the algorithm can be time-varying objects more accurate identification, system stability, robustness . Finally, we use VC 6.0 development of control software , combined with the USB data acquisition card on the gas shuttle kiln control. The realization of the self-priming gas shuttle kiln temperature is more precise control of the atmosphere , the pressure control program control by expert experience , achieved relatively good control effect.