A model predictive controller was designed in this study for a single supply chain unit.A demand model was described using an autoregressive integrated moving average(ARIMA) model,one that is identified on-line to forecast the future demand.Feedback was used to modify the demand prediction,and profit was chosen as the control objective.To imitate reality,the purchase price was assumed to be a piecewise linear form,whereby the control objective became a nonlinear problem.In addition,a genetic algorithm was introduced to solve the problem.Constraints were put on the predictive inventory to control the inventory fluctuation,that is,the bullwhip effect was controllable.The model predictive control(MPC) method was compared with the order-up-to-level(OUL) method in simulations.The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.
Jian NIU Zu-hua XU Jun ZHAO Zhi-jiang SHAO Ji-xin QIAN
The development of an innovative H∞ controller for looper and tension control in hot strip finishing mills is traced based on approximately linearized model. This solution has been considered thanks to its well- known robustness and simplicity characteristics concerning disturbances' attenuation. The controller is designed based on an optimal problem with linear matrix inequality (LMI) constraints, and the problem is solved by the mincx function of Matlab LMI Toolbox. Simulation results show the effectiveness of the proposed controller compared with conventional ones.
