Application of Optimum Adaptive Generalized Predictive Control to Green Tea Drying

One of the most frequently used operations in the processing industry is food drying. This is a complex, multiparameter, and nonlinear dynamic system, the degree of nonlinearity of which is determined by the operating range of the drying process. For a dryer to operate efficiently, it must not only be well designed, but the control strategies implemented must also be effective. And the drying process control system must maintain the necessary controlled variables in the face of many disorders that arise in production situations and the uncertainty of the conditions of the drying process. In the article, to improve the quality of process control under uncertainty in the conditions of the drying process, design methods are applied based on the use of a predictive controller for the state of system parameters using the Box–Wilson optimization method and the “experiment planning.” In general, the results of the simulation of the green tea drying process control system show that the model predictive control (MPC) controller is stable and stable in terms of suppressing input disturbance. The control system of the MPC, when implementing the Box–Wilson method for the object model, provides relatively more efficient operation compared to traditional MPC.