Robust Control Feedback and Learning
Final technical rept. 1 Oct 1997-31 Aug 2001
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES DEPT OF ELECTRICAL ENGINEERING
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The research focused on broadening the class of solvable robust control problems and on developing a firm information theoretic foundation for incorporating the real-time effects of evolving experimental data. Robust control theory concerns the design of control systems capable of robustly maintaining performance to within prescribed tolerances in the face of large-but-bounded modeling uncertainties and nonlinearities. Significant advances were achieved in nonlinear robustness analysis for systems having repeated monotone nonlinearities and in reliable data-driven adaptive control synthesis techniques based on unfalsified control theory. The theory enables design of nonlinear feedback control systems that learn, discover and evolve in order to robustly compensate for battle damage, equipment failures and other changing circumstances.
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