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

• Main Authors: Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Abbas, Kassim Ali
• Format: Article
• Language: English
• Published: SAGE Publications 2011
• Online Access: http://psasir.upm.edu.my/id/eprint/60428/1/Non-linear%20modelling%20and%20control%20of%20a%20conveyor-belt%20grain%20dryer%20utilizing%20neuro-fuzzy%20systems.pdf; http://psasir.upm.edu.my/id/eprint/60428/; http://journals.sagepub.com/doi/abs/10.1177/2041304110394559

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.