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

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

scholarly journals The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 178
Author(s):  
Sebastian Plamowski ◽  
Richard W Kephart
Keyword(s):  
Predictive Control ◽  
Model Order Reduction ◽  
Order Reduction ◽  
High Order ◽  
Double Precision ◽  
Model Order ◽  
Numerical Errors ◽  
Multiple Input ◽  
Multidimensional Objects ◽  
Order Reduction Method

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.

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

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.

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

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

scholarly journals Behavioral Analysis of Various Techniques of Model Order Reduction Used in the Reduction of Large Scale Control System

2019 ◽  
Vol 9 (2) ◽  
pp. 4894-4899
Keyword(s):  
Transfer Function ◽  
Model Order Reduction ◽  
Large Scale ◽  
Transfer Functions ◽  
Order Approximation ◽  
Order Reduction ◽  
Higher Order ◽  
Reduction Technique ◽  
Order System ◽  
Model Order

It is very important task to study the behavior of the processes occurring in the industry. To attain this task, the knowledge of the transfer function of the system should be there. When working in robust environment, these transfer functions becomes so tedious that it becomes very difficult to obtain these transfer functions and hence affects the study of the behavior of these system. Due to this, the requirement for reduction of these transfer function becomes a necessity to analyze the behavior of foresaid systems and it becomes easy to do the desired modifications in the system i.e addition of any feature, desired changes in the behavior etc., furthermore the thing to be kept in consideration while doing the reduction in transfer function that the behavior viz. peak overshoot, settling time, steady state error of the two systems (reduced and the original system) should be approximately same, so it is prime importance that the applied model order reduction technique should provide a more accurate approximation of original higher order system. The paper presents here the different categories of model order reduction techniques that can be applied to achieve the motto of model order reduction of higher order systems. The techniques presented are categorized into the four different categories to understand them and their merits and demerits and these will help in proper selection of the model order reduction technique to obtain the most accurate reduced order approximation of large scale system.

Fast Harmonic MEMS Simulation With Arnoldi Model Order Reduction

2009 ◽  
Author(s):  
David Binion ◽  
Xiaolin Chen
Keyword(s):  
Model Order Reduction ◽  
Krylov Subspace ◽  
Order Reduction ◽  
Simulation Models ◽  
Original Model ◽  
Computational Time ◽  
Mems Devices ◽  
Element Analysis ◽  
Model Order ◽  
Similar Accuracy

Recent years has witnessed a large increase in the use of vibrating Micro-Electro-Mechanical-Systems (MEMS) especially in the expanding wireless telecommunication industry. In particular, the use of microresonators to generate or filter signals has facilitated a reduction in the size of many popular cell phones. Advances in microfabrication have increased the ability to create complex MEMS devices. Finite Element Analysis (FEA) has widely been used in the design of these devices. To obtain accurate simulations of complex MEMS devices, a dense FEA mesh is required resulting in computationally demanding simulation models. Arnoldi Model Order Reduction has been investigated and implemented to improve the computational efficiency of MEMS simulations. Using ANSYS, a popular FEA program, a micro resonator model was created. With Arnoldi, a Krylov subspace was extracted from the model and the model was projected onto the subspace reducing the model size. A harmonic simulation over normal operating frequencies was performed on the reduced model and compared with a simulation of the original model. It was found that the computational time was drastically reduced through the use of Arnoldi while achieving similar accuracy as compared to the original model.

Model order reduction based on discrete-time Laguerre functions for discrete linear periodic time-varying systems

2020 ◽  
Vol 42 (16) ◽  
pp. 3281-3289
Author(s):  
Li-Li Sun ◽  
Kang-Li Xu ◽  
Yao-Lin Jiang
Keyword(s):  
Model Order Reduction ◽  
Transfer Functions ◽  
Order Reduction ◽  
Time Varying ◽  
Laguerre Functions ◽  
Model Order ◽  
Reduced Order ◽  
Engineering Problems ◽  
Time Varying Systems ◽  
Order Reduction Method

Many engineering problems can be modelled as linear periodic time-varying (LPTV) systems, which naturally leads to the need for model order reduction of LPTV systems. This paper investigates a new model order reduction method for discrete LPTV systems. First, the state-space realization in the Fourier-lifted form of discrete LPTV system is constructed by representing periodic matrices in exponentially modulated periodic (EMP) Fourier series. By using Laguerre functions to expand the transfer function of the resulting Fourier-lifted system, the corresponding model order reduction algorithm is developed. Furthermore, the proposed algorithm is used to reduce the discrete LPTV system in the standard-lifted form. Theoretical analysis indicates that the transfer functions of both reduced order systems can match a certain number of moments. Finally, two numerical examples are given to verify the effectiveness of the proposed method.

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