scholarly journals A Robust Computational Technique for Model Order Reduction of Two-Time-Scale Discrete Systems via Genetic Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Othman M. K. Alsmadi ◽  
Zaer S. Abo-Hammour
Keyword(s):  
Genetic Algorithms ◽  
Time Scale ◽  
Model Order Reduction ◽  
Fitness Function ◽  
Order Reduction ◽  
Discrete Systems ◽  
Computational Technique ◽  
Model Order ◽  
New Approach ◽  
Reduced Order

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements ofB,C, andDmatrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

MULTI-TIME-SCALE SYSTEMS MODEL ORDER REDUCTION VIA GENETIC ALGORITHMS WITH EIGENVALUE PRESERVATION

2011 ◽  
Vol 20 (07) ◽  
pp. 1403-1418 ◽  
Author(s):  
ZÁER. S. ABO-HAMMOUR ◽  
OTHMAN M. K. ALSMADI ◽  
ADNAN M. AL-SMADI
Keyword(s):  
Genetic Algorithms ◽  
Time Scale ◽  
Model Order Reduction ◽  
Fitness Function ◽  
Order Reduction ◽  
New Technique ◽  
Dominant Eigenvalue ◽  
Order Model ◽  
Model Order ◽  
Systems Model

A novel substructure (dominant eigenvalue) preserving genetic algorithm approach for model order reduction (MOR) of multi-time-scale systems is presented in this paper. The new technique is formulated based on genetic algorithms (GAs), sub-optimization and estimation. The GA predicts the elements of an upper triangular matrix form of the system state matrix A, defined in state space representation along with the elements of B, C, and D matrices. The GA procedure is based on maximizing the fitness function corresponding to the reciprocal response deviation between the full order model and the reduced order model. The proposed GA model order reduction method is compared to well-known reduction techniques such as the Balanced Schur Decomposition (BSD), proper orthogonal decomposition (POD), and state elimination through balanced realization. Simulation results validate the robustness of the new technique for MOR with eigenvalue preservation.

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.

Trimming a Hazard Logic Tree with a New Model-Order-Reduction Technique

2017 ◽  
Vol 33 (3) ◽  
pp. 857-874 ◽  
Author(s):  
Keith Porter ◽  
Edward Field ◽  
Kevin Milner
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Computational Effort ◽  
Logic Tree ◽  
Model Order ◽  
New Approach ◽  
Reduced Order ◽  
Reduction Techniques ◽  
Possibility Space ◽  
Diagram Approach

The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.

scholarly journals Extracting second-order structures from single-input state-space models: Application to model order reduction

2011 ◽  
Vol 21 (3) ◽  
pp. 509-519 ◽  
Author(s):  
Jérôme Guillet ◽  
Benjamin Mourllion ◽  
Abderazik Birouche ◽  
Michel Basset
Keyword(s):  
State Space ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Second Order ◽  
Input State ◽  
Model Order ◽  
New Approach ◽  
Order Form ◽  
Form Model ◽  
Single Input

Extracting second-order structures from single-input state-space models: Application to model order reductionThis paper focuses on the model order reduction problem of second-order form models. The aim is to provide a reduction procedure which guarantees the preservation of the physical structural conditions of second-order form models. To solve this problem, a new approach has been developed to transform a second-order form model from a state-space realization which ensures the preservation of the structural conditions. This new approach is designed for controllable single-input state-space realizations with real matrices and has been applied to reduce a single-input second-order form model by balanced truncation and modal truncation.

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.

scholarly journals Model Order Reduction for Real-Time Hybrid Simulation: Comparing Polynomial Chaos Expansion and Neural Network methods

2021 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Thomas Simpson ◽  
Roland Pastorino ◽  
Eleni Chatzi ◽  
Bozidar Stojadinovic
Keyword(s):  
Real Time ◽  
Hybrid Model ◽  
Model Order Reduction ◽  
Polynomial Chaos ◽  
Hybrid Simulation ◽  
Order Reduction ◽  
Chaos Expansion ◽  
Model Order ◽  
Reduced Order ◽  
Simulation Time

Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario by combining numerical and physical substructures. To ensure high fidelity of the simulation results, it is often necessary to conduct hybrid simulation in real-time. One of the challenges arising in real-time hybrid simulation originates from high-dimensional nonlinear numerical substructures and, in particular, from the computational cost linked to the computation of their dynamic responses with sufficient accuracy. It is often the case that the simulation time-step must be decreased to capture the dynamic behavior of numerical substructures, thus resulting in longer computation. When such computation takes longer than the actual simulation time, time delays are introduced and the simulation timescale becomes distorted. In such a case, the only viable solution for doing hybrid simulation in real-time is to reduce the order of such complex numerical substructures.In this study, a model order reduction framework is proposed for real-time hybrid simulation, based on polynomial chaos expansion and feedforward neural networks. A parametric case study encompassing a virtual hybrid model is used to validate the framework. Selected numerical substructures are substituted with their respective reduced-order models. To determine the robustness of the framework, parameter sets are defined to cover the design space of interest. A comparison between the full- and reduced-order hybrid model response is delivered. The attained results demonstrate the performance of the proposed framework.

Fast Multiphysics MEMS Simulation With Arnoldi Model Order Reduction

2008 ◽  
Author(s):  
David Binion ◽  
Xiaolin Chen
Keyword(s):  
Model Order Reduction ◽  
Krylov Subspace ◽  
Order Reduction ◽  
Full Scale ◽  
Solution Time ◽  
System Level ◽  
Computational Time ◽  
Mems Devices ◽  
Model Order ◽  
Reduced Order

Modeling and simulation of Micro Electro Mechanical Systems has become increasingly important as the complexity of MEMS devices increases. In particular, thermal effects on MEMS devices has become a growing topic of interest. Through the FEA, detailed solutions can be obtained to investigate the multiphysics coupling and the transient behavior of a MEMS device at the component level. For system-level integration and simulation, the FEA discretization often results in large full-scale models, which can be computationally demanding or even prohibitive to solve. Model order reduction (MOR) was investigated in this study to reduce problem size for complex dynamic system modeling. The Arnoldi method was implemented for MOR to improve the computational efficiency while preserving the input-output behavior of coupled MEMS simulation. Using this method, a low dimensional Krylov subspace was extracted from the full-scale system model. Reduced order solution of the transient temperature distributions was then determined by projecting the system onto the extracted Krylov subspace and solving the reduced system. An electro thermal MEMS actuator was studied for various inputs. To compare results, the full-scale analyses were performed using the commercial FEA program ANSYS. It was found that the computational time of MOR was only a fraction of the full-scale solution time, with the relative errors ranging from 1.1% to 4.5% at different positions on the actuator. Our results show that the reduced order modeling via Alnoldi can significantly decrease the transient analysis solution time without much loss in accuracy for coupled-field MEMS simulation.

scholarly journals Reduced Order Controller Design for Symmetric, Non-Symmetric and Unstable Systems Using Extended Cross-Gramian

Machines ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 48 ◽  
Author(s):  
Azhar ◽  
Zulfiqar ◽  
Liaquat ◽  
Kumar
Keyword(s):  
Model Order Reduction ◽  
Controller Design ◽  
Order Reduction ◽  
Original System ◽  
Computational Effort ◽  
Model Order ◽  
Unstable Systems ◽  
Reduced Order ◽  
The Cross ◽  
Symmetric Systems

In model order reduction and system theory, the cross-gramian is widely applicable. The cross-gramian based model order reduction techniques have the advantage over conventional balanced truncation that it is computationally less complex, while providing a unique relationship with the Hankel singular values of the original system at the same time. This basic property of cross-gramian holds true for all symmetric systems. However, for non-square and non-symmetric dynamical systems, the standard cross-gramian does not satisfy this property. Hence, alternate approaches need to be developed for its evaluation. In this paper, a generalized frequency-weighted cross-gramian-based controller reduction algorithm is presented, which is applicable to both symmetric and non-symmetric systems. The proposed algorithm is also applicable to unstable systems even if they have poles of opposite polarities and equal magnitudes. The proposed technique produces an accurate approximation of the reduced order model in the desired frequency region with a reduced computational effort. A lower order controller can be designed using the proposed technique, which ensures closed-loop stability and performance with the original full order plant. Numerical examples provide evidence of the efficacy of the proposed technique.

scholarly journals Passivity Preserving Model Order Reduction Using the Reduce Norm Method

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 964
Author(s):  
Namra Akram ◽  
Mehboob Alam ◽  
Rashida Hussain ◽  
Asghar Ali ◽  
Shah Muhammad ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
Model Order Reduction ◽  
High Speed ◽  
Order Reduction ◽  
Reliable Operation ◽  
Model Order ◽  
Reduced Order ◽  
Reduction Methods ◽  
On Chip

Modeling and design of on-chip interconnect, the interconnection between the components is becoming the fundamental roadblock in achieving high-speed integrated circuits. The scaling of interconnect in nanometer regime had shifted the paradime from device-dominated to interconnect-dominated design methodology. Driven by the expanding complexity of on-chip interconnects, a passivity preserving model order reduction (MOR) is essential for designing and estimating the performance for reliable operation of the integrated circuit. In this work, we developed a new frequency selective reduce norm spectral zero (RNSZ) projection method, which dynamically selects interpolation points using spectral zeros of the system. The proposed reduce-norm scheme can guarantee stability and passivity, while creating the reduced models, which are fairly accurate across selected narrow range of frequencies. The reduced order results indicate preservation of passivity and greater accuracy than the other model order reduction methods.

Sign in / Sign up

Export Citation Format

Share Document