Particle Board Continuous Press Intelligent Control Algorithm Of Automatic Correction

Wood-based panels is an important method for the efficient utilization of timber resources, particleboard has good sound-absorbing and sound proofing, uniform density, holding good nail strength, transferring compressive strength, moisture resistant; particleboard surface is smooth, texture and wood similar plate thickness deviation, wear-resistant, anti-aging, beautiful, generous, you can paint and have a variety of veneer treatment.In the particleboard production process, the hot pressing process is a very important part. Hot pressing has a direct impact on the quality of particleboard, physical properties and appearance of the good or bad, at the same time, it is also important to affect the cost of the product. In the past10years, we have learned the advanced skills about the West continuous flat press and produced our continuous flat press machine independently; continuous flat press pressure is relatively constant and production has uniform plate thickness. With sanding a small amount of production, we can have much energy savings and cost.In this paper, the process control of continuous flat presses appropriate to improve, we have optimize continuous flat press control systems from different methods. Complexity, nonlinearity and hysteresis characteristics of the hot pressing process, the use of traditional PID control approach to control the hot pressing process, the system can gradually into a stable state, but larger overshoot in the control process, and also have a great impact on the safety performance of the whole device, while the longer time to steady state, so we use the fuzzy PID control, fuzzy control is based on the experience of skilled technical personnel, so the system nonlinearity and hysteresis have a good improvement, fuzzy control’s robustness is strong, but less parameter tuning of the dynamic changes in the system process, so we use BP neural network PID control system. Its online identification, dynamic adjustment of the PID parameters can achieve good results, but for the interference of external processing capacity is weak, poor robustness, and finally we use the of the integrated use of fuzzy neural network PID control dynamic online identification system and interfere with a better treatment. The system can get into steady easily and quickly and have less overshoot. The above control method have simulation by MATLAB and animation simulation in MATLAB GUI man-machine interface while taking advantage of the design parameters, while the preparation of the S function to the Simulink simulation of the system to prove the advantages and shortcomings of each method.