Adaptive For Decoupling Control And Simulation Of Wood Drying System

With the improvment of people's living standards and living conditions, the demand for the quantity and quality of wood has increased higher and higher. It will boost the research and development of wood drying automatic control techniques.Wood drying system is a complex multivariant system. There is a complex internal structure and heavy couplings among variants because of multiinputs and multioutputs. Traditional decoupling controller has a capability of decoupling control to some degree, but its design and implement is troublesome and the decoupling precision is depends on the exactness of the the system's mathematic model. So, a good control performance is difficult to be obtained.The hybrid of neural network and adaptive control provides a new idea for decoupling multivariants wood drying control system.In this paper, to resolve the coupling phenomena between temperature and humidity in wood drying system, a BP neural network based PID controller is proposed and applied to wood drying system. The architecture and learning algorithm of the proposed controller is more simpler and the physical meanings of the input layer's neurons and output layer's neurons are explicit. Based on predefined control rules and self-learning, the BP network changs the scaling integral and differential parameters, therefore is able to control the variants using classical PID control algorithms and at the same time, decoupling control is implemented as well during the control procedure. The decoupling control dose not need the online learning of the given signals. At the end of the paper, a decoupling simulation experiment is conducted and it has shown effective results for controlling wood drying system.