Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems

Abstract

The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control this process. In this context, a laboratory-scale conveyor-belt grain dryer was specifically designed and constructed for this study. Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input-output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. As the main controller, a simplified proportional-integral-derivative (PID)-like ANFIS controller is utilized to control the drying process. A real-coded genetic algorithm (GA) is used to train this controller and to find its scaling factors. From the robustness tests and a comparative study with a genetically tuned conventional PID controller, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process represented by the developed ANFIS model.

Intelligent Modeling and Control of a Conveyor Belt Grain Dryer Using a Simplified Type 2 Neuro-Fuzzy Controller

“…The result of this process is a set of input-output data which can be used to develop the desired system model. In this context, a data collecting experiment was conducted in [23] to obtain the required input-output data set by a realtime paddy drying experiment. The result of this experiment was a set of input-output data samples ready to be used in the system identification process to develop the desired dryer model.…”

“…The proposed controller is a modified version of the original Type-1 ANFIS controller which was proposed in [23,25,35] . The structure of the latter is illustrated in Fig.…”

“…To this end, the second objective of this study was to propose a simplified Type-2 ANFIS controller as an improved version of a previously reported simplified Type-1 ANFIS controller. [23,25,35] It is worth mentioning that, unlike the pervious grain drying control methods, the advantage of the modelling and control techniques adopted in this study is that they are applicable for the drying of any agricultural grains regardless of their size and physical properties. Moreover, the developed nonlinear dryer model can be directly utilized to design the controller, rather Drying Technology than linearizing this model first, which was the common practice followed in previous studies.…”

“…For example, Lutfy et al [23][24][25] utilized the capabilities of the ANFIS network to model and control the grain drying process. In other studies, [26][27][28] a linear TF model was derived for the same dryer considered in [23][24][25] .…”

A GODFIP Control Algorithm for an IRC Grain Dryer

“…These simplifications are expected to affect the performance of these models and consequently their reliability in representing the real process when using these models for control purposes. Moreover, the mathematical models generally consist of sets of highly complex and nonlinear partial differential equations (PDES) with several auxiliary algebraic equations that involve transfer coefficients and thermophysical properties that require highly complicated numerical techniques to solve, rendering them undesirable options in control systems [14].…”