Robust control from data via uncertainty model sets identification

2004 ◽  
Vol 14 (11) ◽  
pp. 945-957 ◽  
Author(s):  
S. Malan ◽  
M. Milanese ◽  
D. Regruto ◽  
M. Taragna
1999 ◽  
Vol 121 (4) ◽  
pp. 433-439 ◽  
Author(s):  
D. E. Cox ◽  
G. P. Gibbs ◽  
R. L. Clark ◽  
J. S. Vipperman

This work addresses the design and application of robust controllers for structural acoustic control. Both simulation and experimental results are presented. H∞ and μ-synthesis design methods were used to design feedback controllers which minimize power radiated from a panel while avoiding instability due to unmodeled dynamics. Specifically, high-order structural modes which couple strongly to the actuator-sensor path were poorly modeled. This model error was analytically bounded with an uncertainty model which allowed controllers to be designed without artificial limits on control effort. It is found that robust control methods provide the control designer with physically meaningful parameters with which to tune control designs and can be very useful in determining limits of performance. However, experimental results also showed poor robustness properties for control designs with ad-hoc uncertainty models. The importance of quantifying and bounding model errors is discussed.


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