Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors
This paper provides a novel combination of Reference Governors (RG) and Iterative Learning Control (ILC) to address the issue of simultaneous learning and constraint management in systems that perform a task repeatedly. The proposed control strategy leverages the measured output from the previous iterations to improve tracking, while guaranteeing constraint satisfaction during the learning process. To achieve this, the system is modeled by a linear system with polytopic uncertainties. An RG solution based on a robust Maximal Admissable Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. An update law on the MAS is proposed to further improve performance.
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