GOALI: Robust Model Predictive Control Using Cost Function Constraints

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Abstract - Badgwell - 9714950 Model Predictive Control (MPC) refers to a class of algorithms that control future plant behavior through the use of an explicit process model. MPC technology is used throughout the petroleum refining and chemical industries. A typical MPC controller adjusts plant inputs once a minute in order to optimize future plant performance. Industrial MPC algorithms use a variety of performance objectives, input parameterizations, and process models. MPC algorithms owe their power to the fact that they have a mathematical description of the process to be controlled. In practice, however, the model is only an approximation of true plant behavior and problems can arise when model errors become significant. The objective of this project is to develop and implement a new family of robust MPC algorithms in which plant uncertainty is incorporated directly into the control formulation. Theoretical and computational issues will be addressed; prototype algorithms will be developed in the Matlab programming language and will be tested on two simulated processes. In addition, field tests are planned on a high purity distillation columns and a polymer reaction system at Exxon Chemical Inc. The verified algorithms will then be incorporated into next-generation MPC products by Aspen Technology, Inc. This is a Grant Opportunities for Academic Liaison with Industry (GOALI) grant.