In this paper we focus on the following loop-shaping problem: Given a nominal plant and QFT bounds, synthesize a controller that achieves closed-loop stability, satisfies the QFT bounds and has minimum high-frequency gain. The usual approach to this problem involves loop shaping in the frequency domain by manipulating the poles and zeroes of the nominal loop transfer function. This process now aided by recently-developed computer-aided design tools, proceeds by trial and error, and its success often depends heavily on the experience of the loop-shaper. Thus, for the novice and first-time QFT users, there is a genuine need for an automatic loop-shaping tool to generate a first-cut solution. Clearly, such an automatic process must involve some sort of optimization, and, while recent results on convex optimization have found fruitful applications in other areas of control design, their immediate usage here is precluded by the inherent nonconvexity of QFT bounds. Alternatively, these QFT bounds can be over-bounded by convex sets, as done in some recent approaches to automatic loop-shaping, but this conservatism can have a strong and adverse effect on meeting the original design specifications. With this in mind, we approach the automatic loop-shaping problem by first stating conditions under which QFT bounds can be dealt with in a non-conservative fashion using linear inequalities. We will argue that for a first-cut design, these conditions are often satisfied in the most critical frequencies of loop-shaping and are violated in frequency bands where approximation leads to negligible conservatism in the control design. These results immediately lead to an automated loop-shaping algorithm involving only linear programming techniques, which we illustrate via an example.

1.
Bailey, F. N., Helton, J. W., and Merino, O., 1994, “Alternative Approaches in Frequency Domain Design of Single Loop Feedback System with Plant Uncertainty,” Procs. ACC, pp. 345–349.
2.
Ballance, D. J., and Gawthrop, P. J., 1991, “Control Systems Design Via a QFT Approach,” Procs. Control 91, pp. 476–481.
3.
Barratt, C., and S. Boyd, S., 1993, “Closed-Loop Convex Formulation of Classical and Singular Value Loop Shaping,” Advances in Control Systems, C. T., Leondes ed. Vol. 55.
4.
Borghesani, C., Chait, Y., and Yaniv, O., 1995, The QFT Control Design MATLAB Toolbox, The MathWorks, Inc., Natick, MA.
5.
Boyd, S., and Vandenberghe, L., 1995, Introduction to Convex Optimization with Engineering Applications, Lecture notes for EE392X, Spring Quarter, Stanford University.
6.
Branch, M. A., and Grace, A., 1996, Optimization Toolbox for MATLAB, The MathWorks, Natick, MA.
7.
Bryant
G. F.
, and
Halikias
G. D.
,
1995
, “
Optimal Loop-Shaping for Systems with Large Parameter Uncertainty via Linear Programming
,”
Int. J. Control
, Vol.
62
, pp.
557
568
.
8.
Gahinet, P., Nemirovski, A., Laub, A. J., and Chilali, M., 1995, LMl Control Toolbox for use with MATLAB, The Math Works Inc., Natick, MA.
9.
Gera
A.
, and
Horowitz
I.
,
1980
, “
Optimization of the Loop Function
,”
International J. Control
, Vol.
31
, pp.
389
398
.
10.
Helton, J. W., and Merino, O., 1994, Classical Control Using H Methods (rough draft), Dept. of Math, UCSD.
11.
Horowitz
I.
,
1973
, “
Optimum Loop Transfer Function in Single-Loop Minimum-Phase Feedback Systems
,”
International J. Control
, Vol.
18
, pp.
97
113
.
12.
Horowitz, 1993, Quantitative Feedback Design Theory (QFT), QFT Publications, 44780 Grinnell Ave., Boulder, CO, 80303, Vol. 1, p 217.
13.
Rodrigues
J. M.
,
Chait
Y.
, and
Hollot
C. V.
,
1997
, “
An Efficient Algorithm for Computing QFT Bounds
,”
ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL
, Vol.
119
, pp.
548
552
.
14.
Thompson
D. F.
, and
Nwokah
O. D. I.
,
1994
, “
Analytic Loop Shaping Methods in Quantitative Feedback Theory
,”
ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL
, Vol.
116
, pp.
169
177
.
15.
Veres, S. M., 1996, Ed., The Geometric Bounding Toolbox for MATLAB, V5.2, The University of Birmingham, UK.
16.
Zhao, Y., and Jayasuriya, S., 1993, “Robust Stabilization of Uncertain Systems with Parametric Uncertainties,” Procs. 12th IFAC Conf., Sydney, Australia, Vol. 6, pp. 31–34.
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