Both Repetitive Control (RC) and Iterative Learning Control (ILC) aim to eliminate tracking errors in feedback control systems that perform repetitive tasks, or experience periodic disturbances, or both. Several enhancements to the performance of RC and ILC are developed here. First, it is observed that an image of a startup jump can exist in the RC command for many periods. RC systems usually eliminate this jump discontinuity slowly due to its high frequency content. The nature of this startup jump image is studied, and various methods are developed to prevent or reduce such discontinuities. Due to modeling error, a low pass filter is usually required to cut off the learning process above some frequency to produce stability robustness. This study shows that peaks in the passband of the cutoff filter force one to use a lower cutoff frequency than one could use otherwise. The second topic therefore develops improvements to the zero-phase low-pass filters used. The improved filter designs use quadratic programming with inequality constraints to improve filter performance and therefore allows a higher cutoff. This filter design can also function as an effective anti-aliasing filter. The third topic considers situations in which the period of the disturbance is not an integer number of time steps, which would normally require interpolation. However, interpolation usually results in substantial error remaining after convergence. Various enhancements to the cutoff filter allow the filter to perform the extra function of adjusting for a fractional time step shift in the disturbance period. Finally, it is shown that various ILC law families can be unified by considering each as a special case of a quadratic cost ILC law with appropriately chosen weight matrices. The stability robustness to magnitude and phase errors in the model is studied for these ILC laws. With this knowledge and the model uncertainty as a function of frequency, one can design a more sophisticated ILC law by adjusting the learning rate in the ILC law associated with each singular value of the system in order to maximize the robustness where needed.
|Advisor:||Longman, Richard W.|
|School Location:||United States -- New York|
|Source:||DAI-B 71/09, Dissertation Abstracts International|
|Keywords:||Filter design, Interative learning control, Periodic disturbances, Quadratic programming, Repetitive control, Tracking errors|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be