Improved Constrained Model Predictive Tracking Control for Networked Coke Furnace Systems Over Uncertainty and Communication Loss
提出一种改进的约束网络化模型预测跟踪控制方法，用于焦炉炉膛压力调节，能同时处理不确定性、数据包丢失和设定点跟踪，相比传统方法有更多设计自由度并提升性能。
This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.