A MEMS design methodology for model-order-reduction, based on high-order parametric elements

The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry.

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A correlation-based model order reduction approach for a diesel engine NOx and brake mean effective pressure dynamic model using machine learning

International Journal of Engine Research ◽

10.1177/1468087420936949 ◽

2020 ◽

pp. 146808742093694

Author(s):

Armin Norouzi ◽

Masoud Aliramezani ◽

Charles Robert Koch

Keyword(s):

Support Vector Machine ◽

Model Order Reduction ◽

Order Reduction ◽

High Order ◽

Support Vector ◽

Effective Pressure ◽

Reduction Algorithm ◽

Order Model ◽

Model Order ◽

Low Order

A correlation-based model order reduction algorithm is developed using support vector machine to model [Formula: see text] emission and break mean effective pressure of a medium-duty diesel engine. The support vector machine–based model order reduction algorithm is used to reduce the number of features of a 34-feature full-order model by evaluating the regression performance of the support vector machine–based model. Then, the support vector machine–based model order reduction algorithm is used to reduce the number of features of the full-order model. Two models for [Formula: see text] emission and break mean effective pressure are developed via model order reduction, one complex model with high accuracy, called high-order model, and the other with an acceptable accuracy and a simple structure, called low-order model. The high-order model has 29 features for [Formula: see text] and 20 features for break mean effective pressure, while the low-order model has nine features for [Formula: see text] and six features for break mean effective pressure. Then, the steady-state low-order model and high-order model are implemented in a nonlinear control-oriented model. To verify the accuracy of nonlinear control-oriented model, a fast response electrochemical [Formula: see text] sensor is used to experimentally study the engine transient [Formula: see text] emissions. The high-order model and low-order model support vector machine models of [Formula: see text] and break mean effective pressure are compared to a conventional artificial neural network with one hidden layer. The results illustrate that the developed support vector machine model has shorter training times (5–14 times faster) and higher accuracy especially for test data compared to the artificial neural network model. A control-oriented model is then developed to predict the dynamic behavior of the system. Finally, the performance of the low-order model and high-order model is evaluated for different rising and falling input transients at four different engine speeds. The transient test results validate the high accuracy of the high-order model and the acceptable accuracy of low-order model for both [Formula: see text] and break mean effective pressure. The high-order model is proposed as an accurate virtual plant while the low-order model is suitable for model-based controller design.

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Design Low Order Robust Controller for the Generator’s Rotor Angle Stabilization PSS System

Emerging Science Journal ◽

10.28991/esj-2021-01299 ◽

2021 ◽

Vol 5 (5) ◽

pp. 598-618

Author(s):

Vu Ngoc Kien ◽

Nguyen Hien Trung ◽

Nguyen Hong Quang

Keyword(s):

Model Order Reduction ◽

Design Method ◽

Order Reduction ◽

High Order ◽

Optimal Order ◽

Robust Controller ◽

Model Order ◽

Rotor Angle ◽

Load Angle ◽

Low Order

The electrical system's problem stabilizes the electrical system with three primary parameters: rotor angle stability, frequency stability, and voltage stability. This paper focuses on the problem of designing a low-order stable optimal controller for the generator rotor angle (load angle) stabilization system with minor disturbances. These minor disturbances are caused by lack of damping torque, change in load, or change in a generator during operation. Using the RH∞optimal robust design method for the Power System Stabilizer (PSS) to stabilize the generator’s load angle will help the PSS system work sustainably under disturbance. However, this technique's disadvantage is that the controller often has a high order, causing many difficulties in practical application. To overcome this disadvantage, we propose to reduce the order of the higher-order optimal robust controller. There are two solutions to reduce order for high-order optimal robust controller: optimal order reduction according to the given controller structure and order reduction according to model order reduction algorithms. This study selects the order reduction of the controller according to the model order reduction algorithms. In order to choose the most suitable low-order optimal robust controller that can replace the high-order optimal robust controller, we have compared and evaluated the order-reducing controllers according to many model order reduction algorithms. Using robust low-order controllers to control the generator’s rotor angle completely meets the stabilization requirements. The research results of the paper show the correctness of the controller order reduction solution according to the model order reduction algorithms and open the possibility of application in practice. Doi: 10.28991/esj-2021-01299 Full Text: PDF

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The Combined Methodology for Reducing the High Model Order for a Continuous System Using Genetic Algorithm and Dominant Poles Methods

International Journal of Contemporary Research and Review ◽

10.15520/ijcrr/2018/9/06/531 ◽

2018 ◽

Vol 9 (06) ◽

pp. 20447-20458

Author(s):

Mohammad A. ALMa’aitah ◽

Mohammed Al-Hattab ◽

Mohammed I. Abuashour ◽

Tha’er O. Sweidan ◽

Omar M. Abdallah

Keyword(s):

Genetic Algorithm ◽

Lower Order ◽

Model Order Reduction ◽

Transfer Functions ◽

Order Reduction ◽

Continuous System ◽

Original Model ◽

High Order ◽

Model Order ◽

Improving Accuracy

Model order reduction is one of the crucial topics facing researchers nowadays. Various methods were conducted for achieving this goal. In this article, genetic algorithm (GA) with dominant poles methods are used to reduce high-order transfer functions (TFs) to lower-order ones. Genetic algorithm is powerful technique used for optimization purposes. In this approach, genetic algorithm is applied to model order reduction to reduce the order of the numerator of TF whereas the dominant poles method is used to reduce the order of denominator of the TF and thus improving accuracy and preserving the same dominant poles for the reduced system as the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original high order models being reduced

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Model order reducibility of nonlinear electro-quasistatic problems

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-12-2018-0519 ◽

2019 ◽

Vol 38 (5) ◽

pp. 1453-1464

Author(s):

Fotios Kasolis ◽

Markus Clemens

Keyword(s):

Domain Decomposition ◽

Model Order Reduction ◽

Design Methodology ◽

Order Reduction ◽

Computational Domain ◽

Computationally Efficient ◽

Model Order ◽

Content Type ◽

Strongly Nonlinear ◽

Domain Decomposition Algorithm

Purpose This paper aims to develop an automated domain decomposition strategy that is based on the presence of nonlinear field grading material, in the context of model order reduction for transient strongly nonlinear electro-quasistatic (EQS) field problems. Design/methodology/approach The paper provides convincing empirical insights to support the proposed domain decomposition algorithm, a numerical investigation of the performance of the algorithm for different snapshots and model order reduction experiments. Findings The proposed method successfully decomposes the computational domain, while the resulting reduced models are highly accurate. Further, the algorithm is computationally efficient and robust, while it can be embedded in black-box model reduction implementations. Originality/value This paper fulfills the demand to effectively perform model order reduction for transient strongly nonlinear EQS field problems.

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