Parametric macromodeling of lossy and dispersive multiconductor transmission lines

We propose an innovative parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs) that can be used for interconnect modeling. It is based on a recently developed method for the analysis of lossy and dispersive MTLs extended by using the multivariate orthonormal vector fitting (MOVF) technique to build parametric macromodels in a rational form. They take into account design parameters, such as geometrical layout or substrate features, in addition to frequency. The presented technique is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels allow to perform design space exploration, design optimization, and sensitivity analysis efficiently. Numerical examples validate the proposed approach in both frequency and time domain

Constrained LQR Using Online Decomposition Techniques

This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints. Second, the MPC problem is decomposed into smaller subproblems of fixed dimension independent of the horizon length. Third, using the fast alternating minimization algorithm to solve the subproblems, the horizon length is estimated online, by adding or removing subproblems based on a periodic check on the state of the last subproblem to determine whether it belongs to a given control invariant set. We show that the estimated horizon length is bounded and that the control sequence computed using the proposed algorithm is an optimal solution of the CLQR problem. Compared to state-of-the-art algorithms proposed to solve the CLQR problem, our design solves at each iteration only unconstrained least-squares problems and simple gradient calculations. Furthermore, our technique allows the horizon length to decrease online (a useful feature if the initial guess on the horizon is too conservative). Numerical results on a planar system show the potential of our algorithm.Comment: This technical report is an extended version of the paper titled "Constrained LQR Using Online Decomposition Techniques" submitted to the 2016 Conference on Decision and Contro

Non intrusive polynomial chaos-based stochastic macromodeling of multiport systems

We present a novel technique to efficiently perform the variability analysis of electromagnetic systems. The proposed method calculates a Polynomial Chaos-based macromodel of the system transfer function that includes its statistical properties. The combination of a non-intrusive Polynomial Chaos approach with the Vector Fitting algorithm allows to describe the system variability features with accuracy and efficiency. The results of the variability analysis performed with the proposed method are verified by means of comparison with respect to the standard Monte Carlo analysis

Time-domain parametric sensitivity analysis of multiconductor transmission lines

We present a new parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs). This technique can handle design parameters, such as substrate or geometrical layout features, and provide time-domain sensitivity information for voltage and currents at the ports of the lines. It is based on a recently introduced spectral approach for the analysis of lossy and dispersive MTLs [1], [2] and it is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels which provide sensitivity information are well suited for design space exploration, design optimization and crosstalk analysis. A numerical example validates the proposed approach in both frequency and time domain

Parameterized model order reduction of delayed systems using an interpolation approach with amplitude and frequency scaling coefficients

When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. We present an innovative PMOR technique for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, amplitude and frequency scaling coefficients and positive interpolation schemes. It is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach

Stochastic macromodeling for hierarchical uncertainty quantification of nonlinear electronic systems

A hierarchical stochastic macromodeling approach is proposed for the efficient variability analysis of complex nonlinear electronic systems. A combination of the Transfer Function Trajectory and Polynomial Chaos methods is used to generate stochastic macromodels. In order to reduce the computational complexity of the model generation when the number of stochastic variables increases, a hierarchical system decomposition is used. Pertinent numerical results validate the proposed methodology

Passivity-preserving parameterized model order reduction for PEEC based full wave analysis

We present a novel parameterized model order reduction technique applicable to the Partial Element Equivalent Circuit method that is able to generate parametric reduced order models, stable and passive by construction, over a user defined design space. Overall stability and passivity of the parametric reduced order model are guaranteed by an efficient and reliable combination of traditional passivity-preserving model order reduction methods and interpolation schemes based on a class of positive interpolation operators. A pertinent numerical example validates the proposed parameterized model order reduction approach

Fast identification of Wiener-Hammerstein systems using discrete optimisation

A fast identification algorithm for Wiener-Hammerstein systems is proposed. The computational cost of separating the front and the back linear time-invariant (LTI) block dynamics is significantly improved by using discrete optimisation. The discrete optimisation is implemented as a genetic algorithm. Numerical results confirm the efficiency and accuracy of the proposed approach

Short communication: Molecular genetic characterization of ovine αS1-casein allele H caused by alternative splicing

Abstract Sequencing of ovine CSN1S1*H cDNA showed an absence of exon 8 in comparison with GenBank sequences; the absence was confirmed by protein sequencing. We demonstrated that this allelic aberration is the result of a deletion of 4 nucleotides, the last 3 of exon 8 and the first 1 of intron 8, which are replaced by an insertion of 13 nucleotides in the DNA sequence. The insertion is a precise duplication of a part of the adjacent intronic sequence of CSN1S1*C″ . These sequence differences result in an inactivation of the splice donor sequence distal to exon 8, leading to upstream exon skipping during the serial splice reactions of the ovine CSN1S1*H pre-mRNA, and may affect the specific casein expression as well as protein characteristics