NEURO-FUZZY CONTROL OF THE LUMBER DRYING PROCESS
Background. The problem of control over the process of lumber drying is considered. The quality of drying is determined by the modes of operation of power plants that provide heat supply to the drying chamber and the parameters of the moisture content of the dried sawn timber. Recently, in many wo...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN RU |
| Publicado: |
Penza State University Publishing House
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/57b29d538d824d75bdb94c2485c28d27 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Background. The problem of control over the process of lumber drying is considered. The quality of
drying is determined by the modes of operation of power plants that provide heat supply to the drying chamber and the
parameters of the moisture content of the dried sawn timber. Recently, in many works, the process of drying sawn
timber is considered as an optimal control problem, in which the material to be dried must achieve the specified state
by its properties in a minimum time. Materials and methods. To determine the modes of high-quality optimal control
and effective change of these modes in the process of drying control, it is necessary to have at each moment of time
the exact values of the parameters of the model of the controlled object. These values cannot be accurately determined
using measuring instruments. Results and conclusions. Thus, the process of optimally managing the drying of lumber
involves uncertainties. To eliminate the problem of uncertainties in the work, it is proposed to use the mathematical
apparatus of fuzzy sets to describe them, which, in the process of fuzzification of variables, will translate the undefined
values of the model parameters into linguistic terms with certain membership functions. To obtain control actions
based on the analysis of linguistic variables, it is proposed to use a neuro-fuzzy control system with Tagaki–
Sugeno–Kang logical inference based on the ANFIS neural network, which implements optimal control of sawn timber
drying based on the rule base set by the developers of the control system. |
|---|