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FUNDING ($K) TRANSITIONS “Stability region analysis using sum-of-squares programming,”

Development of Analysis Tools for Certification of Flight Control Laws UC Berkeley, Andrew Packard, Honeywell, Pete Seiler, U Minnesota, Gary Balas. Region-of-attraction Disturbance-to-error gain Verify set containments in state-space with SOS proof certificates. Long-Term PAYOFF

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FUNDING ($K) TRANSITIONS “Stability region analysis using sum-of-squares programming,”

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  1. Development of Analysis Tools for Certification of Flight Control Laws UC Berkeley, Andrew Packard, Honeywell, Pete Seiler, U Minnesota, Gary Balas . Region-of-attraction Disturbance-to-error gain Verify set containments in state-space with SOS proof certificates. • Long-Term PAYOFF • Direct model-based analysis of nonlinear systems • OBJECTIVES • Develop robustness analysis tools applicable to certification of flight control laws: quantitative analysis of locally stable, uncertain systems • Complement simulation with Lyapunov-based proof techniques, actively using simulation • Connect Lyapunov-type questions to MilSpec-type measures of robustness and performance Convex outer bound Aid nonconvex proof search (Lyapunov fcn coeffs) with constraints from simulation FUNDING ($K) TRANSITIONS “Stability region analysis using sum-of-squares programming,” 2006 American Control Conference, pp. 2297-2302. “Local gain analysis of nonlinear systems,” 2006 American Control Conference, pp. 92-96 STUDENTS, POST-DOCS Ufuk Topcu, Tim Wheeler, Weehong Tan LABORATORY POINT OF CONTACT Dr. Siva Banda, Dr. David Doman • APPROACH/TECHNICAL CHALLENGES • Analysis based on Lyapunov/storage fcn method • Non-convex sum-of-squares (SOS) optimization • Merge info from conventional simulation-based assessment methods to aid in the nonconvex opt • Unfavorable growth in computation: state order, vector field degree and # of uncertainties. • Reliance on SDP and BMI solvers, which remain under development, unstable and unreliable • ACCOMPLISHMENTS/RESULTS • Pointwise-max storage functions • Parameter-dependent storage functions • Benefits of employing simulations

  2. Constraints from simulation effectively aid nonconvex search for Lyapunov function proving region-of-attraction “radius” Region of attraction estimate for samples • Unseeded PENBMI solutions • (red) • 500 simulations • 50 samples of Lyapunov outer bound set • PENBMI solutions seeded with samples. Improved estimate, consistent and reliable execution Convex outer bound Convex Constraints on Lyapunov coefficients, obtained from simulation. Initialize nonconvex search within this set PENBMI results, seeded with samples A. Packard/ UC Berkeley, P. Seiler/Honeywell, G. Balas / University of Minnesota

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