Two-Layered Model Predictive Control Strategy of the Cut Tobacco Drying Process

Abstract

In this article, a two-layered model predictive control strategy is proposed for the nonsquare system of nonlinear cut tobacco drying process. The control objective is to optimize the drum dryer temperature, hot air temperature, and cut tobacco outlet temperature meet the process constraints while meeting the moisture content of cut tobacco. Firstly, the tobacco drying process system was introduced, and the nonsquare system model and performance index function were established. Then a nonlinear moving horizon estimator (NMHE) and real-time optimization (RTO) are designed. NMHE provides state and parameter estimation for the controller, and RTO provides an optimal operating setpoint for the controller. Subsequently, a two-layered model predictive control (SSTO-MPC) design integrated with a steady-state target optimization layer (SSTO) is proposed for the nonsquare system of nonlinear cut tobacco drying process. Extensive simulations under different scenarios illustrate the effectiveness of the proposed SSTO-MPC design compared with the conventional MPC.

Keywords

• Cut tobacco drying process
• moving horizon estimation
• model predictive control
• nonsquare system
• steady-state target optimization
• real-time optimization