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

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

It is a choice of articles by means of associates, co-authors, colleagues, and scholars of Keith Glover, Professor of Engineering on the college of Cambridge, at the social gathering of his sixtieth birthday. Professor Glover's paintings spans various themes, together with approach id, version aid and approximation, strong controller synthesis, and regulate of plane and engines. the gathering is a tribute to Professor Glover's seminal paintings in those parts.

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14, pp. 651–660, 2004. 2. P. Apkarian, J. M. Biannic, and P. Gahinet, “Self-scheduled H∞ control of missile via linear matrix inequalities,” AIAA Journal on Guidance, Control and Dynamics, vol. 18, pp. 532–538, 1995. LMI-Based Gain Scheduling 23 3. F. Bruzelius, S. Petterson, and C. Breitholtz, “Linear parameter-varying descriptions of nonlinear systems,” in Proc. of the 2004 American Control Conference, 2004. 4. J. S. Shamma and M. Athans, “Analysis of gain scheduled control for nonlinear plants,” IEEE Transactions on Automatic Control, vol.

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. A two DOF longitudinal axis model for control design purposes was proposed in [3]. The simplified model is linear within the cavity and nonlinear when the vehicle is in contact with the cavity (planing).

Then, ¯ i is fixed for some i and the optimization is repeated for a hyper-cube of λ lower dimension, and so forth. By this strategy, eventually, a large polytope symmetric around λ = 0 for which the LMIs are satisfied will be found, allowing for correspondingly large parameter variations. ¯ a bisection algorithm may be To find a good trade-off between σ and λ, run for different values of σ, where the quasi-convex optimization strategy outlined above can be carried out for each chosen value of σ. One has to employ a bisection algorithm (or similar technique) to optimise σ because σ enters the expressions for Φ(s), F (s), F ⊥ (s), G(0, s), .

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