UWSpace >
University of Waterloo >
Electronic Theses and Dissertations (UW) >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10012/3850

Title: On A New Approach to Model Reference Adaptive Control
Authors: Naghmeh, Mansouri
Keywords: adaptive control
linear time-varying control
model reference adaptive control
H-infinity framework
Approved Date: 12-Aug-2008
Date Submitted: 24-Jul-2008
Abstract: The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters. Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [1]. In this new approach, instead of estimating plant parameters, the “ideal control signal” (what the control signal would be if the plant parameters and states were measurable) is estimated. The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [1] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree. Here we extend this work in several directions: (i) In [1], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size, (ii) A key drawback of the controller of [1] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem, (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant, (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability, and (v) A number of approaches to improve the noise behaviour of the controller are presented. Reference: [1] D. E. Miller, “A New Approach to Model Reference Adaptive Control”, IEEE Transaction on Automatic Control, Vol. 48, No. 5, pages 743-756, May 2003.
Program: Electrical and Computer Engineering
Department: Electrical and Computer Engineering
Degree: Doctor of Philosophy
URI: http://hdl.handle.net/10012/3850
Appears in Collections:Faculty of Engineering Theses and Dissertations
Electronic Theses and Dissertations (UW)

Files in This Item:

File Description SizeFormat
Thesis Naghmeh Mansouri, 2008.pdf2.01 MBAdobe PDFView/Open


This item is protected by original copyright

All items in UWSpace are protected by copyright, with all rights reserved.

 

University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

contact us | give us feedback | http://www.lib.uwaterloo.ca | © 2006 University of Waterloo