|
Server : Apache/2.4.62 System : FreeBSD fbsdweb2.web.rcn.net 14.1-RELEASE FreeBSD 14.1-RELEASE releng/14.1-n267679-10e31f0946d8 GENERIC amd64 User : www ( 80) PHP Version : 8.3.8 Disable Function : NONE Directory : /domains/astrosfm/AAS_meetings/1998_winter/abstracts/ |
Upload File : |
<title>AAS 98-119</title><body BGCOLOR="ffffff"> <h2>AAS 98-119</h2> <h2>BRIDGING LEARNING CONTROL AND REPETITIVE CONTROL USING BASIS FUNCTIONS</h2> <h4>Hao-Ping Wen - Columbia Universtiy; M. Q. Phan - Princeton Universtiy; R. W. Longman - Columbia University</h4> <h2> Abstract </h2> The field of repetitive control develops control algorithms that learn to eliminate repetitive errors in a feedback controller's response to a periodic command or in its response to a constant command with a periodic disturbance. The typical objective is to attempt to eliminate all tracking error as time progresses for each time step of the period. It is often difficult to obtain this convergence based on the nominal system model, and even when convergence is guaranteed based on the nominal model, phase errors at high frequencies, for example due to unmodeled low amplitude high frequency dynamics, can destroy the convergence. This paper studies the use of projections of the error histories on chosen basis functions sets as a means to obtain improved robustness of repetitive control. The first set of basis function addressed comes from Fourier series, and has the advantage that the understanding of the stability boundary based on frequency response can be applied. Then other sets of basis functions including the use of wavelets are considered, and the potential advantages and disadvantages are investigated. <P><A HREF="http://www.space-flight.org/AAS_meetings/1998_winter/sessions.html">Back</A>