Modeling and Control of an Indirect Solar Dryer in Forced Convection Mode Using System Identification and Model Predictive Control

Abstract

In nonlinear process, system modeling is vital part to predict and examine the performance of a plant. Here, indirect solar dryer performed in forced convection mode taken as a plant. Several experimental tests were performed by drying tomato slices, both in forced as well as in natural convection mode to study the dynamic characteristics of it. The efficiency of indirect solar dryer was better with forced convection than the natural convection mode. In this investigation System Identification technique is used to predict the model equation for forced convection. Designing a conventional control strategy to ensure efficiency is cumbersome due to the plant’s multivariable nature and nonlinear dynamics. The regular proportional-integral-derivative (PID) controller provides unproductive control action for nonlinear system. A model predictive controller (MPC) is intended for the proposed system ensuring efficiency of the indirect solar dryer. The MPC parameters must be selected appropriately to ensure optimal performance. The Matlab platform was used to get the both MPC algorithms and conventional PID performance.