Genetic Algorithm–Based Robust Controller for an Inverted Pendulum Using Model Order Reduction

In recent years, more and more scientists have been interested in research on driving two-wheel bicycles. The problems in two-wheel bicycle control problem are self-balancing, uncertain models, and the impact of noise. In the paper, to solve the self-balancing problem, we use the flywheel method according to the inverted pendulum principle. To overcome the effects of the uncertain model, the impact of noise, we designed the vehicle balance controller according to the robust control algorithm. However, robust controllers often have a high order, which affects the quality during real control. To simplify the robust controller, we propose the use of a model order reduction algorithm. The simulation and experimental results have proved the correctness of the solutions given in the paper.

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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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