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Embedded Model Predictive Control on a PLC Using a Primal-DualFirst-Order Method for a Subsea Separation Process

机译:基于原始 - 对偶一阶方法的pLC在海底分离过程中的嵌入式模型预测控制

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摘要

The results of a PLC implementation of embedded Model Predictive Control (MPC) for an industrial problem are presented in this paper. The embedded MPC developed is based on the linear MPC module in SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control), and it combines custom ANSI C code generation with problem size reduction methods, embedded real-time considerations, and a primal-dual first-order method that provides a fast and light QP solver obtained from the FiOrdOs code generator toolbox. Since the primal-dual first-order method proposed in this paper is new in the control community, an extensive comparison study with other state-of-the-art first-order methods is conducted to underline its potential. The embedded MPC was implemented on the ABB AC500 PLC, and its performance was tested using hardware-in-the-loop simulation of Statoil's newly patented subsea compact separation process. A warm-start variant of the proposed first-order method outperforms a tailored interior-point method by a factor of 4 while occupying 40% less memory.
机译:本文介绍了针对工业问题的嵌入式模型预测控制(MPC)的PLC实现结果。开发的嵌入式MPC基于SEPTIC(用于识别和控制的静油估计和预测工具)中的线性MPC模块,并将定制的ANSI C代码生成与问题大小减小方法,嵌入式实时注意事项和原始对偶结合在一起一阶方法,提供了从FiOrdOs代码生成器工具箱获得的快速轻巧的QP解算器。由于本文提出的原对偶一阶方法在控制领域是新的,因此与其他最新的一阶方法进行了广泛的比较研究,以强调其潜力。嵌入式MPC在ABB AC500 PLC上实现,并使用Statoil最新获得专利的海底压实分离工艺的硬件在环仿真测试了其性能。拟议的一阶方法的热启动变体比量身定制的内点法性能高出4倍,而占用的内存却少了40%。

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