Temperature Control of Monocrystalline Silicon Heating Furnace Based on WOA Optimized FNN-PID
To address the problems of large inertia,strong coupling and long adjustment time in the temperature control of monocrystalline silicon heating furnace,an optimized fuzzy neural network(FNN)proportional-integral-differential(PID)algorithm based on whale optimization algorithm(WO A)was proposed.By testing the temperature of the experimental setups,a model expression was derived.The WOA was used to iteratively optimize and find appropriate PID parameters.The FNN was used to adjust PID parameters in real time to achieve dynamic decoupling.Simulation verification was carried out by simulation software,and step response experiment and signal tracking experiment were conducted on the constructed model,respectively.The simulation results show that compared with traditional PID algorithm and FNN-PID algorithm,the optimized FNN-PID algorithm based on WOA effectively improves the heating speed of the system without overshoot.The heating experiment results of the heating furnace show that the maximum temperature overshoot is 0.9 ℃,and the temperature control precision in the constant temperature zone is maintained at±0.3 ℃.These results indicate that this method can effectively improve the heating speed and stability of the system.
multi-temperature zone temperature controlwhale optimization algorithm(WOA)fuzzy neural network(FNN)proportion-integral-differential(PID)monocrystalline silicon heating furnace
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维普
