Study Intelligent Variable Structure Control For Wood Drying Process

With the reduction of global forest resources day-by-day and the appearance of environmental protection and ecological problems,governments all over the world paid attention to these problems such as how to use the limited wood resources reasonably,reduce energy resource consumption and increase the quality of wood products.Faced by our country with fewer forest resources,how to improve using quantity and raise utilization ratio is one of the most important projects.Wood drying is an important measure for improving wood physical mechanics performance,ultilizing wood reasonably,reducing loss of lower class and raising wood utilization ratio.It is one of the key technologies for guaranteeing the quality of wood products.Wood drying is a complicated non-linear process that is lagged,time-variable and coupling.For them,it is difficult to build up ideal drying model,which is corresponding to reality.It is difficult to control automatically because there are so many affecting factors. Traditional PID controlling method lacks sufficient flexibility.It is urgent to build up modern control system of high quality for innovative drying equipment.In the disertation,the wood drying models were built up based on Neural Network.The Neural Network was identified by studying input and output data.It made the goal functions minimum,thus implying the relationship of input data and output data,namely the model describing system.The temperature-humidity controlling model and drying schedule model were built up with the Time Delay Neural Network and Dynamical Recurrent Neural Network which were suitable for identifying non-linear system.Temperature-humidity controlling model was the model that dicribed the relationship among three controlling signals including heat-valve,spray-valve,eliminating-damp-valve and temperature and humidity,which offered the essential conditions for designing controller.Drying schdule model discribed the relationship between temperature-humidity and wood moisture content,which provided the mathematic model for drying schedule,predicted for the changing characters of moisture content and offered the effective basis for optimizing the drying standard.Drying schedule inverse model could provide the values of temperature and humidity in drying kiln at this moment according to the moisture content,thus making the stage drying in succession which vied moisture content schedule as drying schedule.In our study the structure of the Neural Network and learning algorithms were given,in addition,we trained and verified the model through experimental data.The simulation results showed that the models were effective and reliable.The controlling course of wood drying was a process of controlling the temperature and humidity of the medium in kiln to make the wood moisture content reduce to a certain expected value.According to the characteristic of real dry course,we examined the ultiliazation of variable structure control and fuzzy variable structure control in wood drying control system based on Neural Network model and the closed-loop stability was proved by using Lyapunov method.In order to show the effectiveness and feasibility of the proposed methods,the numerical simulation results show that the two kinds of control methods were better than traditional PID,adaptive PID and fuzzy PID control.This was the basis for improving controlling level of wood drying and realizing controlling wood-drying course fully automatically.It was importance for guaranteeing the drying quality and reducing the energy consumption and the cost effectively.