摘要
木材干燥过程是一个强耦合、大滞后的非线性动力系统,很难准确建立被控对象的数学模型。为了准确控制木材干燥过程的温度和湿度,提高木材干燥质量,将智能控制引入木材干燥控制系统是必然的发展趋势。结合模糊控制和神经网络优点,设计了一种木材干燥窑内温湿度的Takagi-Sugeno(T-S)型模糊神经网络控制器。该控制器无需对象的精确数学模型,适应性强,利用模糊算法解除木材干燥窑内温度和湿度间的强耦合关系,采用神经网络的自学习和自适应能力来实现整个非线性过程的模糊逻辑推理。仿真和实验结果表明,T-S型模糊神经网络控制器有效解决了木材干燥过程的温湿度控制,控制器响应速度快、超调小、鲁棒性强、控制精确度高,可以满足木材干燥控制系统要求。
Wood drying process presents normally the non-linear characteristics of strong coupling and large lagging, therefore, it is hardly to build the math model of controlled object. In order to control more precisely the temperature and humidity of the wood drying process so as to improve the drying quality, it is necessary to apply the intelligent controller in wood drying control system. Combining the merits of fuzzy control and neural control, a Takagi-Sugeno （T-S） fuzzy neural network controller is designed to control the inner temperature and humidity of wood drying kiln. This controller has strong adaptability and did not depend on the precise math model. With fuzzy algorithm, the coupling relationship was removed between inner temperature and humidity of wood drying kiln. The self-learning and adaptive ability of neural net- work was used to accomplish the fuzzy logic of the whole non-linear process. The simulation reveales that T-S fuzzy neural network controller solves the problem of low control precision of temperature and humidi- ty in wood drying process. And this controller has fast response speed, low overshoot, strong robustness and high control precision. It might fulfill the demand of wood drying control system.
出处
《电机与控制学报》
EI
CSCD
北大核心
2016年第10期114-120,共7页
Electric Machines and Control
基金
国家林业公益性行业科研专项(201304502)
关键词
木材干燥过程
T-S模型
模糊神经网络控制器
温湿度控制
神经网络
wood drying process
Takagi-Sugeno model
fuzzy neural network controller
control of tem-perature and humidity
neural network
