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Closed-loop dynamic real-time optimization with stabilizing model predictive control
AIChE Journal ( IF 3.7 ) Pub Date : 2021-05-14 , DOI: 10.1002/aic.17308
Praveen Sundaresan Ramesh 1 , Christopher L. E. Swartz 1 , Prashant Mhaskar 1
Affiliation  

Dynamic real-time optimization (DRTO) is a supervisory strategy at the upper level of the industrial process automation architecture that computes economically optimal set-point trajectories that are in turn passed on to the lower-level model predictive control (MPC) for tracking. The economically optimal solution, in several process industries, could lead to operating the plant at or around an unstable steady state. The present article accounts for this by developing a closed-loop DRTO (CL-DRTO) formulation that enables handling unstable operating points via an underlying MPC with stability constraints. To this end, a stabilizing MPC that handles trajectory tracking for unstable systems is embedded within the upper-level DRTO. The resulting CL-DRTO problem is reformulated by applying a simultaneous solution approach. The economic benefits realized by the proposed strategy are illustrated through applications to both linearized and nonlinear dynamic models for single-input single-output and multi-input multi-output continuous stirred tank reactor case studies.

中文翻译:

具有稳定模型预测控制的闭环动态实时优化

动态实时优化 (DRTO) 是工业过程自动化架构上层的一种监管策略,它计算经济上最优的设定点轨迹,然后传递给下层模型预测控制 (MPC) 进行跟踪。在几个过程工业中,经济上的最佳解决方案可能会导致工厂在不稳定的稳定状态或附近运行。本文通过开发闭环 DRTO (CL-DRTO) 公式来解释这一点,该公式能够通过具有稳定性约束的基础 MPC 处理不稳定的工作点。为此,在上层 DRTO 中嵌入了一个处理不稳定系统轨迹跟踪的稳定 MPC。由此产生的 CL-DRTO 问题通过应用同步求解方法重新表述。
更新日期:2021-05-14
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