Enhancing Model Order Reduction for Nonlinear Analog Circuit Simulation

Aridhi, Henda; Zaki, Mohamed H.; Tahar, Sofiène

doi:10.1109/tvlsi.2015.2421450

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…Let a square matrix be A and a vector be b, apply the Krylov subspace technique, then a standard Krylov subspace K m {A, b} such as m is its dimension is obtained [13], [14], [15]:…”

Section: Standard Krylov Subspacementioning

confidence: 99%

Lyapunov-Global-Lanczos Algorithm for Model Order Reduction & Adaptive PI Controller of Large Scale Electrical Systems

2017

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Abstract-Mathematical modeling of complex electrical systems, has led us to linear mathematical models of higher order. Consequently, it is difficult to analyse and to design a control strategy of these systems. The order reduction is an important and effective tool to facilitate the handling and designing of a control strategy. In this paper we present, firstly, a reduction method which is based on the Krylov subspace and Lyapunov techniques, that we call Lyapunov-Global-Lanczos. This method minimizes the H∞ norm error, absolute error and preserves the stability of the reduced system. It also provides a better reduced system of order 1, with closer behaviour to the original system. This first order system is used to design PI (Proportional-Integral) controller. Secondly, we implement an adaptive digital PI controller in a microcontroller. It calculates the PI parameters in real time, referring to the error between the desired and measured outputs and the initial values of PI controller, that were determined from the first order system. Two simulation examples and a real time experimentation are presented to show the effectiveness of the proposed algorithms.

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“…Let a square matrix be A and a vector be b, apply the Krylov subspace technique, then a standard Krylov subspace K m {A, b} such as m is its dimension is obtained [13], [14], [15]:…”

Section: Standard Krylov Subspacementioning

confidence: 99%

Lyapunov-Global-Lanczos Algorithm for Model Order Reduction & Adaptive PI Controller of Large Scale Electrical Systems

2017

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“…The concept of model order reduction has been extend to neutral type control system (systems with time delays present both in their state and state derivatives) [6], [7]. Discussions within [8], explore the concept of model reduction to lessen the mathematical complexities of nonlinear analog circuits. In the last twenty years, there has been a significant surge of interest in employing model reduction techniques to address the intricacies present in electromagnetic systems with higher dimensions [9].…”

Section: Introductionmentioning

confidence: 99%

Using a New Model Reduction Controllers Technique for Design a Large-Scale Linear Systems

Monika,

Mishra

2023

Preprint

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A innovative technique is introduced for decreasing the complexity of linear dynamical systems of higher order systems. Here, a generalized technique for grouping of poles is employ to find the reducing order plant’s denominators polynomial and numerator of polynomial is determine the using of time moment matching method. The algorithm for the grouping of generalized poles ensures that the maintenance of steadiness and prominent poles from the original system within the plant of reduced order. The suggested technology is supported by performances of error matrices such as many integrals error like absolute error, square error, time-weighted of absolute error and relative square error. A moment matching approach is used to create proportional integral derivative and lead/lag compensators using the simplified plant's transfer function. The original large-scale system is then modified to incorporate this controller, yielding a closed-loop response that closely resembles the characteristics response of the targeted references model.

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“…Model reduction is introduced to deal with such problem, which aims to find a simplified reduced-order model to approximate the original complex high-order model. Model reduction has been widely used in many engineering fields such as power systems [34], filters design [35], [36], circuit simulations [37], [38]. Besides, in the past few decades, many methods have been introduced for solving the problem of model reduction such as Hankel norm based methods [39], [40], H ∞ norm based methods [41], [42], H 2 norm based methods [43], and H 2 -H ∞ based methods [44].…”

Section: Introductionmentioning

confidence: 99%