A novel series structure is proposed based on cascade control for dominating a class of unstable processes with large delay-times. The modified structure consists mainly of one controller that exists in the inner loop, and the inner loop controller is devised on the basis of internal model control (IMC) principles. The outer loop setpoint tracking controller and the inner loop load disturbance rejection controller are designed according to the H2 optimal performance. At the same time, the tuning parameters are selected through the experience approach. Moreover, a suitable example and real value are recommended for the inner loop controller based on a wide-ranging simulation, and the results are implemented to certify the effectiveness of the proposed method. Simultaneously, the proposed method is applied to in a real-world tobacco production line, and the test results prove that the above method is significant in terms of rapidity, robustness and residual errors.INDEX TERMS Modified cascade control, moisture control process, large delay-time, IMC.
In this paper, 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.
In this paper, an L1-Norm model predictive controller with a dead band zone is proposed for the cut tobacco drum dryer system. The control objective is to make the drum dryer temperature, hot air temperature and cut tobacco outlet temperature meet the process constraints, and optimize the outlet moisture content of the cut tobacco. First, the cut tobacco drum dryer system is introduced, and the nonlinear open equation model is established. Then an L1-Norm moving horizon estimator (MHE) is designed to provide state and parameter estimation for the controller by using its ability to deal with nonlinearity and constraints. A model predictive control (L1-Norm zone MPC) for L1-Norm target tracking with a dead band zone is proposed for the cut tobacco drum dryer system. The simulation results show that the proposed L1-Norm zone MPC (L1-ZMPC) better-tracking performance and the controller's minimum action economic characteristics compared with the traditional setpoint tracking model predictive control.
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