Robust Iterative Learning Control for Linear Parameter-Varying Systems with Time Delays
thesisposted on 30.07.2020 by Florian M Browne
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
The work in this dissertation concerns the construction of a robust iterative learning control (ILC) algorithm for a class of systems characterized by measurement delays, parametric uncertainty, and linear parameter varying (LPV) dynamics. One example of such a system is the twin roll strip casting process, which provides a practical motivation for this research. I propose three ILC algorithms in this dissertation that advance the state of the art. The first algorithm compensates for measurement delays that are longer than a single iteration of a periodic process. I divide the delay into an iterative and residual component and show how each component effects the asymptotic stability properties of the ILC algorithm. The second algorithm is a coupled delay estimation and ILC algorithm that compensates for time-varying measurement delays. I use an adaptive delay estimation algorithm to force the delay estimate to converge to the true delay and provide stability conditions for the coupled delay estimation and ILC algorithm. The final algorithm is a norm optimal ILC algorithm that compensates for LPV dynamics as well as parametric uncertainty and time delay estimation error. I provide a tuning method for the cost function weight matrices based on a sufficient condition for robust convergence and an upper bound on the norm of the error signal. The functionality of all three algorithms is demonstrated through simulated case studies based on an identified system model of the the twin roll strip casting process. The simulation testing is also augmented with experimental testing of select algorithms through collaboration with an industrial sponsor.