Model uncertainty, if ignored, can seriously degrade the performance of an otherwise well-designed control system. If the level of this uncertainty is extreme, the system may even be driven to instability. In the context of structural control, performance degradation and instability imply excessive vibration or even structural failure. Robust control has typically been applied to the issue of model uncertainty through worst-case analyses. These traditional methods include the use of the structured singular value, as applied to the small gain condition, to provide estimates of controller robustness. However, this emphasis on the worst-case scenario has not allowed a probabilistic understanding of robust control. In this paper an attempt to view controller robustness as a probability measure is presented. The probability of failure due to parametric uncertainty is estimated using first-order reliability methods (FORM). It is demonstrated that FORM can provide quite accurate results on the probability of fai...
[1]
M. Sain,et al.
Probabilistic stability measures for controlled structures subject to real parameter uncertainties
,
1992
.
[2]
T. T. Soong,et al.
Experiments on Active Control of Seismic Structures
,
1988
.
[3]
Chang-Hee Won,et al.
Reliability-based measures of structural control robustness
,
1994
.
[4]
Robert F. Stengel,et al.
A monte carlo approach to the analysis of control system robustness
,
1993,
Autom..
[5]
T. T. Soong,et al.
Experimental Study of Active Control for MDOF Seismic Structures
,
1989
.
[6]
Lawrence A. Bergman,et al.
Computation of probabilistic stability measures for a controlled distributed parameter system
,
1995
.
[7]
R. Stengel,et al.
Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems
,
1991
.