Routh Approximation: An Approach of Model Order Reduction in SISO and MIMO Systems

2016 ◽  
Vol 2 (3) ◽  
pp. 486 ◽  
Author(s):  
D. K. Sambariya ◽  
Omveer Sharma
Keyword(s):  
Mimo Systems ◽  
Order Reduction ◽  
Reduced Order Model ◽  
Step Response ◽  
Order Model ◽  
Model Order ◽  
Single Input Single Output ◽  
Reduced Order ◽  
Single Output ◽  
Single Input

In this paper the Routh Approximation method is explored for getting the reduced order model of a higher order model. The reduced order modeling of a large system is necessary to ease the analysis of the system. The approach is examined and compared to single-input single-output (SISO) and multi-input multi-output (MIMO) systems. The response comparison is considered in terms of step response parameters and graphical comparisons. It is reported that the reduced order model using proposed Routh Approximation (RA) method is almost similar in behavior to that of with original systems.

scholarly journals Suboptimal Control Using Model Order Reduction

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Avadh Pati ◽  
Awadhesh Kumar ◽  
Dinesh Chandra
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Higher Order ◽  
Reduced Order Model ◽  
Suboptimal Control ◽  
Order Model ◽  
Model Order ◽  
Markov Parameters ◽  
Reduced Order ◽  
Partial Feedback

A Padé approximation based technique for designing a suboptimal controller is presented. The technique uses matching of both time moments and Markov parameters for model order reduction. In this method, the suboptimal controller is first derived for reduced order model and then implemented for higher order plant by partial feedback of measurable states.

Model order reduction for quasi-magnetostatic problems

2017 ◽  
Vol 36 (6) ◽  
pp. 1783-1791
Author(s):  
Yuqing Xie ◽  
Lin Li ◽  
Shuaibing Wang
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Reduced Order Model ◽  
Order Method ◽  
Computational Accuracy ◽  
Order Model ◽  
Model Order ◽  
Content Type ◽  
Reduced Order ◽  
Magnetostatic Problem

Purpose To reduce the computational scale for quasi-magnetostatic problems, model order reduction is a good option. Reduced-order modelling techniques based on proper orthogonal decomposition (POD) and centroidal Voronoi tessellation (CVT) have been used to solve many engineering problems. The purpose of this paper is to investigate the computational principle, accuracy and efficiency of the POD-based and the CVT-based reduced-order method when dealing with quasi-magnetostatic problems. Design/methodology/approach The paper investigates computational features of the reduced-order method based on POD and CVT methods for quasi-magnetostatic problems. Firstly the construction method for the POD and the CVT reduced-order basis is introduced. Then, a reduced model is constructed using high-fidelity finite element solutions and a Galerkin projection. Finally, the transient quasi-magnetostatic problem of the TEAM 21a model is studied with the proposed reduced-order method. Findings For the TEAM 21a model, the numerical results show that both POD-based and CVT-based reduced-order approaches can greatly reduce the computational time compared with the full-order finite element method. And the results obtained from both reduced-order models are in good agreement with the results obtained from the full-order model, while the computational accuracy of the POD-based reduced-order model is a little higher than the CVT-based reduced-order model. Originality/value The CVT method is introduced to construct the reduced-order model for a quasi-magnetostatic problem. The computational accuracy and efficiency of the presented approaches are compared.

scholarly journals Model Order Reduction Technique Applied on Harmonic Analysis of a Submerged Vibrating Blade

2019 ◽  
Vol 24 (1) ◽  
pp. 131-142 ◽  
Author(s):  
E. Tengs ◽  
F. Charrassier ◽  
M. Holst ◽  
Pål-Tore Storli
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Reduction Technique ◽  
Reduced Order Model ◽  
Order Model ◽  
Model Order ◽  
Reduced Order ◽  
Speed Up ◽  
Speedup Factor ◽  
Fully Coupled

Abstract As part of an ongoing study into hydropower runner failure, a submerged, vibrating blade is investigated both experimentally and numerically. The numerical simulations performed are fully coupled acoustic-structural simulations in ANSYS Mechanical. In order to speed up the simulations, a model order reduction technique based on Krylov subspaces is implemented. This paper presents a comparison between the full ANSYS harmonic response and the reduced order model, and shows excellent agreement. The speedup factor obtained by using the reduced order model is shown to be between one and two orders of magnitude. The number of dimensions in the reduced subspace needed for accurate results is investigated, and confirms what is found in other studies on similar model order reduction applications. In addition, experimental results are available for validation, and show good match when not too far from the resonance peak.

scholarly journals Model Order Reduction for Lumped RC Transmission Lines

2020 ◽  
Author(s):  
Farid N. Najm
Keyword(s):  
Transmission Lines ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Reduced Order Model ◽  
Order Model ◽  
Model Order ◽  
Reduced Order ◽  
Detailed Review ◽  
Circuit Description ◽  
Rc Networks

<div>We start with a detailed review of the PACT approach for model order reduction of RC networks. We then develop a method that uses PACT as a preprocessing step to transform a generic lumped RC transmission line of some nominal order, based on a nominal (r,c) setting, into a parameterized circuit captured in a SPICE sub-circuit description. Then, given any other lumped RC line of the same order, we pass its (r,c) setting as parameters to this sub-circuit so as to automatically transform and reduce the line into a reduced order model without having to rerun PACT. In this way, we effectively characterize lumped RC transmission lines in a way that allows them to be reduced on-the-fly without any expensive processing.</div>

Firefly artificial intelligence technique for model order reduction with substructure preservation

2019 ◽  
Vol 41 (10) ◽  
pp. 2875-2885 ◽  
Author(s):  
Othman Alsmadi ◽  
Adnan Al-Smadi ◽  
Esra’a Gharaibeh
Keyword(s):  
Artificial Intelligence ◽  
Model Order Reduction ◽  
Fitness Function ◽  
Order Reduction ◽  
Reduced Order Model ◽  
Order Model ◽  
Artificial Intelligence Technique ◽  
Model Order ◽  
Reduced Order ◽  
Intelligence Technique

Model order reduction (MOR) is a process of finding a lower order model for the original high order system with reasonable accuracy in order to simplify analysis, design, modeling and simulation for large complex systems. It is desirable that the reduced order model preserves the fundamental properties of the original system. This paper presents a new MOR technique of multi-input multi-output systems utilizing the firefly algorithm (FA) as an artificial intelligence technique. The reduction operation is proposed to maintain the exact dominant dynamics in the reduced order model with the advantage of substructure preservation. This is mainly possible for systems that are characterized as multi-time scale systems. Obtaining the reduced order model is achieved by minimizing the fitness function that is related to the error between the full and reduced order models’ responses. The new approach is compared with recently published work on firefly optimization for MOR, in addition to three other artificial intelligence techniques; namely, invasive weed optimization, particle swarm optimization and genetic algorithm. As a result, simulations show the potential of the FA for the process of MOR.

scholarly journals POD-DEIM Based Model Order Reduction for the Spherical Shallow Water Equations with Turkel-Zwas Finite Difference Discretization

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Pengfei Zhao ◽  
Cai Liu ◽  
Xuan Feng
Keyword(s):  
Computational Complexity ◽  
Shallow Water ◽  
Shallow Water Equations ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Reduced Order Model ◽  
Order Model ◽  
Time Step ◽  
Model Order ◽  
Reduced Order

We consider the shallow water equations (SWE) in spherical coordinates solved by Turkel-Zwas (T-Z) explicit large time-step scheme. To reduce the dimension of the SWE model, we use a well-known model order reduction method, a proper orthogonal decomposition (POD). As the computational complexity still depends on the number of variables of the full spherical SWE model, we use discrete empirical interpolation method (DEIM) proposed by Sorensen to reduce the computational complexity of the reduced-order model. DEIM is very helpful in evaluating quadratically nonlinear terms in the reduced-order model. The numerical results show that POD-DEIM is computationally very efficient for implementing model order reduction for spherical SWE.

scholarly journals An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

2021 ◽  
Vol 14 (1) ◽  
pp. 157-168
Author(s):  
Hadeel Abdullah ◽  
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Linear Quadratic Regulator ◽  
Reduced Order Model ◽  
Linear Quadratic ◽  
Order Model ◽  
Swarm Optimization ◽  
Model Order ◽  
Reduced Order ◽  
Lqr Controller

The diverse engineering and scientific applications are stated through complex and high-order systems. The significant difficulties of these systems are the complications of modeling, analyzing, and controlling. It is easier to examine simpler models for more physical insights than more complex models and result in lower-ordering controllers that are easier to implement. The model order reduction (MOR) was used to simplify the computational difficulty of such complications and was later developed intensively for use with increasingly CDS. In this paper, a new Modified Chaos Particle Swarm Optimization (MCPSO) technique is employed to get a reduced-order model of a large scale system and design a Linear Quadratic Regulator (LQR) based controller. The mod uses the combination of advantages of basic PSO algorithms and chaotic algorithms. It becomes an excellent algorithm with fast convergence, few control parameters, simple execution, and avoidance of local extremes. In addition to combining the chaotic algorithm, CPSO also improved the weight parameter w, adjusting it to the dynamic attenuation direction. First, efficient reduced-order model parameters are obtained for original higher-order systems based on the MCPSO. Then linear quadratic regulator (LQR (controller parameters optimized for the reduced-order model. The goodness of the proposed method is evaluated through a numerical example. The experimental results indicate that the proposed technique’s reduced order model provides an excellent close approximation to the original system.

An Identification Method With Direct Acquisition of Reduced Order Model From a Steplike Response

2004 ◽  
Vol 126 (4) ◽  
pp. 746-752 ◽  
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Hiroshi Shibata
Keyword(s):  
Order Model ◽  
Output Data ◽  
Mass System ◽  
Single Input Single Output ◽  
Reduced Order ◽  
Identification Method ◽  
Single Output ◽  
Rational Transfer ◽  
Line Identification ◽  
Single Input

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady-state output response. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0-N-tuple integral values of output error and irrelevant to the unmodeled dynamics. From the intermediate parameters, the co-efficients of a rational transfer function are calculated. In consequence, the method can be executed for any linear single-input single-output plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a two-mass system.

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