By Stefano Grivet-Talocia, Bjorn Gustavsen
"In the 1st entire remedy of passive macromodeling out there, macromodeling specialists Stefano Grivet-Talocia and Bjorn Gustavsen handle the complicated topic with examples of potent, confirmed equipment. ultimately, scholars and researchers could flip to a textual content that has a tendency to the theoretical historical past necessary to comprehending the algorithms' merits and downsides. With the newest info on black-box passive macromodeling and software program implementation, this e-book is a foolproof advisor to either the fundamentals and complexities of passive macromodeling"--
"This booklet bargains insurance of advancements in linear macromodeling, with a spotlight on potent, confirmed equipment. After beginning with a definition of the elemental homes that needs to represent types of actual platforms, the authors talk about numerous sought after passive macromodeling algorithms for lumped and disbursed platforms and examine them lower than accuracy, potency, and robustness standpoints. The booklet comprises chapters with general historical past fabric (such as linear time-invariant circuits and platforms, uncomplicated discretization of box equations, state-space systems), in addition to appendices accumulating uncomplicated proof from linear algebra, optimization templates, and signs and transforms. The textual content additionally covers extra technical and complicated subject matters, meant for the professional, that may be skipped initially reading"-- �Read more...
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Extra info for Passive macromodeling : theory and applications
The concepts of energy and power will be introduced in more detail later. 6 Let us consider an ideal amplifier, whose output y(t) is expressed in terms of the input u(t) as y(t) = α(t)u(t), where α(t) is some prescribed gain, possibly time-varying. Intuitively, an amplifier is intended to provide “larger” outputs than the corresponding inputs. This condition is verified if α(t) ≥ A > 1, at least in some time interval t ∈ (t0 , t1 ). Let us then choose an input signal defined as u(t) = 1 if t ∈ (t0 , t1 ), 0 otherwise.
Several definitions of stability exist that apply in different situations. For instance, the bounded-input bounded-output (BIBO) stability imposes a strong requirement on the system, which must produce a bounded signal as a response to a bounded excitation. For the particular case of rational macromodels, this requirement translates into a constraint on the location of the model poles, which must be confined into the left half of the complex plane (asymptotic stability). Other less stringent definitions are sometimes needed, as will be discussed in Chapter 2.
1 S/m). ˘ = Y(jω ˘ samples Y k k ) are regarded as an “exact” representation of the line behavior, albeit only at discrete frequencies. 10(a) depicts the frequency-dependent magnitude of Y˘11 and Y˘12 . The second step involves processing this admittance data to obtain a macromodel. 10(a). We see that accuracy is excellent, since the model responses closely match the reference. 8) reveals that one of the eigenvalues λi is negative in a narrow frequency band close to DC (s = 0). This passivity violation occurs because the rational macromodel, although very accurate, is only an approximation of the true responses.