By Roberto Tempo, Giuseppe Calafiore, Fabrizio Dabbene
"The presence of uncertainty in a procedure description has constantly been a serious factor up to the mark. relocating on from previous stochastic and strong keep watch over paradigms, the most target of this booklet is to introduce the reader to the basics of probabilistic tools within the research and layout of doubtful structures. utilizing so-called randomized algorithms, this rising zone of analysis promises a discount within the computational complexity of classical strong keep an eye on algorithms and within the conservativeness of equipment like H[subscript [infinity]] control."--Jacket. learn more... Overview.- parts of chance Theory.- doubtful Linear platforms and Robustness.- Linear strong keep an eye on Design.- a few Limits of the Robustness Paradigm.- Probabilistic tools for Robustness.- Monte Carlo Methods.- Randomized Algorithms in platforms and Control.- likelihood Inequalities.- Statistical studying idea and keep an eye on Design.- Sequential Algorithms for Probabilistic powerful Design.- Sequential Algorithms for LPV Systems.- situation process for Probabilistic strong Design.- Random quantity and Variate Generation.- Statistical thought of Radial Random Vectors.- Vector Randomization Methods.- Statistical conception of Radial Random Matrices.- Matrix Randomization Methods.- purposes of Randomized Algorithms.- Appendix
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Extra resources for Randomized algorithms for analysis and control of uncertain systems
W 2 If G is represented in the frequency domain by the transfer matrix G(s), then it can be shown that its L+ 2 -gain coincides with the so-called H∞ norm of G(s), deﬁned as . e. G(s) ∞ = G +. 18) is bounded. 2 (H∞ space). The space H∞ is deﬁned as the space of funcp,q tions G : C → C that are analytic and bounded in the open right-half plane. From this deﬁnition it follows immediately that RH∞ ⊂ H∞ . 1 (H∞ norm interpretations). 19) as the maximum energy gain of the system. e. it denotes the maximum average power ampliﬁcation from input to output.
45) 42 3. 9 (Interval matrices). An obvious question that was studied in depth in the area of parametric robustness is whether the stability results for interval and polytopic polynomials can be extended to uncertain matrices. e. a matrix whose entries are bounded in given intervals. Formally, a family A of interval matrices is deﬁned as . + A = A ∈ Rn,n : [A]i,k ∈ [a− ik , aik ], i, k = 1, . . , n . 46) Unfortunately, it has been shown that extensions of Kharitonov-like results to stability of interval matrices fail, see for instance .
20 3. Uncertain Linear Systems and Robustness Denote with v(t) a zero-mean, stationary stochastic process. The autocorrelation of v(t) is deﬁned as . Rv,v (τ ) = Ev v(t)vT (t + τ ) where Ev (·) denotes the expectation with respect to the stochastic process. The power spectral density (psd) Φv,v (ω) of v is deﬁned as the Fourier transform of Rv,v (τ ). A frequently used measure of a stationary stochastic signal is its root-mean-square (rms) value v 2 rms = Ev vT (t)v(t) = Tr Rv,v (0). e. it is not aﬀected by transients.