Date of Award


Document Type


Degree Name

Master of Science (MS)


Mechanical Engineering

First Advisor

Hamid Ossareh

Second Advisor

James Bagrow


In this work, 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 is proposed. 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 plant is modeled by a linear system with uncertainties. An RG solution based on a robust Maximal Admissible Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. The proposed method is applied to the Scalar Reference Governor (SRG), the Vector Reference Governor (VRG) and the Command Governor (CG). An update law on the MAS is proposed to further improve performance.



Number of Pages

66 p.

Included in

Engineering Commons