Control of Uncertain Systems: Modelling, Approximation, and by Bruce A. Francis, Malcolm C. Smith, Jan C. Willems

By Bruce A. Francis, Malcolm C. Smith, Jan C. Willems

It is a choice of articles through pals, co-authors, colleagues, and scholars of Keith Glover, Professor of Engineering on the college of Cambridge, at the get together of his sixtieth birthday. Professor Glover's paintings spans a number of subject matters, together with method id, version aid and approximation, strong controller synthesis, and keep an eye on of airplane and engines. the gathering is a tribute to Professor Glover's seminal paintings in those components.

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Additional info for Control of Uncertain Systems: Modelling, Approximation, and Design: A Workshop on the Occasion of Keith Glover’s 60th Birthday

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Anderson, “Physically based parameterizations for designing adaptive algorithms,” Automatica, vol. 23, pp. 469–477, 1987. 7. D. J. Leith, A. Tsourdos, B. A. White, and W. E. Leithead, “Application of velocity-based gain-scheduling to lateral autopilot design for an agile missile,” Control Engineering Practice, vol. 9, pp. 1079–1093, 2001. 8. L. Zadeh and Desoer, Linear Systems Theory. Krieger Publishing Company, 1979. 9. B. D. O. Anderson, S. Dasgupta, P. Khargonekar, F. J. Kraus, and M. Mansour, “Robust strict positive realness: Characterization and construction,” IEEE Transactions on Circuits and Systems, vol.

The paper concludes with a summary of the results and directions for future study. 2 Modeling of Supercavitating Vehicles Several mathematical descriptions of high speed supercavitation vehicles (HSSVs) are available in the literature [3, 6, 8]. A brief synopsis of these models follows. • • A high fidelity, nonlinear six degrees-of-freedom (DOF) mathematical model was proposed in [8]. Lookup tables are used to calculate the forces generated by control surfaces as a function of immersion depth. The centerline of the cavity bubble is a function of the time history of the path of the vehicle nose affected by buoyancy.

N3 } have been found, construct π = ΠB 1 l×q 1×1 N2 ˘ ˘ ˘ ˘ RH∞ , γ = Γ Bτ2 (s) ∈ RH∞ and ξ = Ξ0 Ξ1 . . ΞN3 BτN33 (s) ∈ (p+1)×(p+1) RH∞ . (p+1)×(p+1) l×q 6. Define α = γ/π ∈ RH∞ and β = ξ/π ∈ RH∞ , and model reduce if necessary (one should check that the reduced quantities still satisfy the LMIs). 7. Compute ˜Λ (s) V˜Λ (s) = α(s)F ⊥ (s) + F T (s)β(s) −U as described in (19) and construct C(λ, s) as given in (14). ˆ s) = C(λ, s+σ). 8. Shift the controller by σ to obtain the final controller C(λ, ˆ s) and Pˆ (λ, s) will then have degree of The closed loop involving C(λ, stability σ > 0 for all (frozen) λ ∈ λbox , and will also be guaranteed to be exponentially asymptotically stable with degree of stability γ ∈ [0, σ) for all time-varying λ(t) ∈ λbox that satisfy conditions (9) and (10) in Theorem 1.

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