The Direct Neural Control Applied to the Position Control in Hydraulic Servo System

Yuan, Kun; Chen, Yiwei; Chang, Yeon‐Pun; Chu, Ming-Huei

doi:10.1007/978-3-540-87734-9_21

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Therefore, the magnetorheological fluid (MRF) valve was designed and has proven to control fluid flow. [7][8][9][10][11]. The MRF valve has successfully eliminated the use of a spool to control fluid flow by manipulating the MRF rheological properties using a magnetic field.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the design process requires knowledge of fluid flow inside the valve while a magnetic field is applied. Salloom and Samad (2012) [13] and Imaduddin et al (2014) [8] developed an MRF valve that worked without a spool, but fluid behaviour in MRF valves have yet to be understood. Due to a lack in knowledge about MRF flow, the response of the valve is difficult to predict and the development of an optimal control system becomes slow.…”

Section: Introductionmentioning

confidence: 99%

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CFD Analysis of Magnetorheological Fluid Single Valve with an External Magnetic Field

Bakar¹,

Mohd-Kamal²,

Samad³

2019

IJRTE

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Magnetorheological fluid (MRF) actuator emerged in the last decade as a potential system to replace electro-hydraulic servo system in precision applications. MRF actuator was control using a valve which is regulated with magnetic field. Because of valve performance relate with flow characteristic inside the valve, knowledge about the flow behaviour when magnetic field is applied was important. Currently analytical method was used to model the valve but limited into simple geometry. Reflect to the need of complex geometry of valve, CFD approached was used in this research work to model the fluid flow with presence of different magnetic strength magnitude as a result CFD model shows a good agreement with experiment with 3% error and the MRF velocity was reduce up to 85% when magnetic field applied. As conclusion, CFD model successfully develop and proven to be used as a tool in analysing the MRF flow

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CFD Analysis of Magnetorheological Fluid Single Valve with an External Magnetic Field

Bakar¹,

Mohd-Kamal²,

Samad³

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Dördüncü katmanda ise birinci dereceden polinom ile ilgili değiştirilebilen üç parametre {pj, qj, rj} vardır. Referans reaktif güç ( ref Q ) ile TKR'nin bağlı olduğu güç sisteminin anlık reaktif gücü ( anlık Q ) arasındaki hata ( e ) öncül ve sonuç parametrelerini ayarlamak için kullanılır[15][16][17][18][19].Her biri üç üyelik fonksiyonu ile bulanıklaştırılmış iki giriş ve bir çıkışa sahip beş katmanlı bir yapay sinir ağı yapısına sahip bulanık mantık algoritmasını içeren 9 kurallı olarak önerilen sinirsel bulanık denetleyici yapısı Şekil 4'te gösterilmektedir. Burada daire biçimde gösterilmiş olan düğüm noktaları sabit olup, kare biçiminde olan düğüm noktaları ise uyarlanabilirdir.Şekil 4.…”

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Tristör Kontrollü Reaktörün Sinirsel Bulanık Denetim Esaslı Reaktif Güç Kontrolü

Keçecioğlu

Kılıç

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, the reactive power of thyristor controlled reactor (TCR) that is fundamental element of flexible ac transmission system devices is controlled using neuro-fuzzy controller. Adaptive neuro fuzzy inference system (ANFIS) is used as neuro-fuzzy control architecture. A simulation model was developed in MATLAB / Simulink environment to examine the performance of the proposed controller. Figure A. Proposed Method for Reactive Power Control of Thyristor Controlled Reactor Purpose: Conventional Proportional Integral (PI) controller is mostly used flexible AC transmission system (FACTS) devices. However, system parameter variations and non-linear system structures severely impair the performance of this controller. In this study, the neuro-fuzzy controller is used for overcome to these disadvantages. Theory and Methods: The neuro-fuzzy controller is designed for reactive power control of TCR. Adaptive neuro-fuzzy inference system (ANFIS) is used as neuro-fuzzy control architecture. In order to examine the performance of the proposed controller, two different cases are simulated in the studies. The reactive power control performance of the proposed controller is compared to two PI controllers. Results: Simulation results show that the tracking of the reference reactive power of proposed controller is more successfully than to PI controllers. The results that are obtained under the voltage disturbance condition show that, the neuro-fuzzy controller is better disturbance rejection capability than PI controllers. Conclusion: In this study, the neuro-fuzzy controller is used for reactive power control of TCR. The proposed controller for TCR is more superior than conventional PI controllers in terms of control performance parameters (rising time, settling time and overshoot) values. The proposed controller has improved the reactive power control capabilities of thyristor controlled reactor.

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The Direct Neural Control Applied to the Position Control in Hydraulic Servo System

Cited by 2 publications

References 6 publications

CFD Analysis of Magnetorheological Fluid Single Valve with an External Magnetic Field

CFD Analysis of Magnetorheological Fluid Single Valve with an External Magnetic Field

Tristör Kontrollü Reaktörün Sinirsel Bulanık Denetim Esaslı Reaktif Güç Kontrolü

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