{ "article_id": 1104, "public_id": "47e0900e-bde9-4375-805e-ffa2a3e81847", "internal_identifier": null, "source_identifier": "42:109", "status": "published", "type": "research_article", "title": "Gürültülü ve Gürültüsüz Ortamlarda Servo Sistemin Kalman Durum Kestirimi ve LQR Kontrolü", "title_secondary": null, "abstract": "Doğru akım motoru servo sistemlerde önemli bir tahrik unsurdur. Hız veya konum denetimi gerektiren sistemlerde kolay denetlenebildiği ve yüksek performans gösterdiği için sıkça kullanılmaktadırlar. Bu çalışmada, bir DC servo sistemin modellenmesi, durum değişkenlerinin Kalman Filtreleri ile gürültülü ve gürültüsüz ortamlarda elde edilmesi ve Doğrusal Kuadratik Regülatör (Liner Quadratic Regulator, LQR) ile optimal kontrolü gerçekleştirilmiştir. Çalışmada öncelikle servo sistemin matematiksel modeli elde edilerek Matlab Simulink modeli oluşturulmuştur. Daha sonra, durum geri besleme ile sistemin LQR kontrolü gerçekleştirilmiştir. Bu yöntemle sistem kontrolü için gerekli tasarım, kapalı çevrim kutuplarının maniplasyonuna gerek kalmadan optimal bir şekilde hesaplanabilmektedir. Bunun yanında fiziksel sistemlerde durum değişkenlerinin tamamının ölçümünün mümkün olmadığı göz önüne alındığında, sistemin durum değişkenlerinin en doğru şekilde elde edilmesi kontrol performansı için önemli bir etken olmaktadır. Bundan dolayı, çalışmada durum değişkenleri Kalman durum kestirimi ile elde edilmiştir. Motor sistemine işlem ve ölçüm gürültüsü uygulanarak denetleyicinin gürültülü ve gürültüsüz ortamlardaki performansı klasik yöntemlerle karşılaştırılmıştır. Böylece gürültülü ortamlarda az hatayla çalışıp az etkilenen, durumları öncesinde tahmin ederek gerçek bilinmeyen değerlerin tahminlerini belirsizlikleriyle üreten yüksek seviyede kararlılığa sahip bir algoritma geliştirilmiştir. Simülasyon sonuçlarına göre, denetleyici performansının tatmin edici olduğu anlaşılmaktadır.", "language": "tr", "english_title": "Kalman State Estimation and LQR Control of Servo System in Noisy and Noiseless Environment", "english_abstract": "DC motor is an important drive element in servo systems. They are frequently used in systems requiring speed or position control because they are easy to control and perform well. In this study, a DC servo system is modeled, state variables are obtained by using Kalman Filters in noisy and noiseless environments and optimal control with Linear Quadratic Regulator (LQR) is performed. In the study, mathematical model of the servo system was obtained and Matlab Simulink model was created. The LQR control of the system was then performed with state feedback. With this method, the design required for system control can be calculated optimally without the need for manipulation of the closed-loop poles. In addition, considering that it is not possible to measure all of the state variables in physical systems, the most accurate way to obtain the state variables of the system is an important factor for the control performance. Therefore, in the study, the state variables were obtained by Kalman state estimation. By applying process and measurement noise to the servo system, the performance of the controller in noisy and noiseless environments was compared with the classical methods. In this way, an algorithm with high stability has been developed which produces predictions of the actual unknown values with uncertainty by predicting the conditions and working with low error in noisy environments. According to the simulation results, the controller performance is satisfactory.", "doi": null, "doi_url": null, "resource_url": "https://www.set-science.com/a/47e0900e-bde9-4375-805e-ffa2a3e81847", "pdf_url": "https://www.set-science.com/a/47e0900e-bde9-4375-805e-ffa2a3e81847/pdf", "og_image": "https://www.set-science.com/assets/img/og-card-v2.png", "authors": [ { "public_id": "8b92b0f6-a9f4-43c9-9a5a-bcfcb2776dfa", "given": "Nedime Merve   ", "family": "Aydın", "full": "Nedime Merve    Aydın", "literal": null, "orcid": null, "orcid_url": null, "email": "mrvecrn07@gmail.com", "corresponding": true, "sequence": "first", "affiliations": [ { "name": "Konya Technical University  ", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] }, { "public_id": "3b1d1d94-ddce-4ea0-824d-3d25d1745d3d", "given": "Ömer   ", "family": "Aydoğdu", "full": "Ömer    Aydoğdu", "literal": null, "orcid": null, "orcid_url": null, "email": "mrvecrn07@gmail.com", "corresponding": false, "sequence": "additional", "affiliations": [ { "name": "Konya Technical University  ", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] } ], "keywords": [], "translations": [ { "language": "en", "title": "Kalman State Estimation and LQR Control of Servo System in Noisy and Noiseless Environment", "abstract": "DC motor is an important drive element in servo systems. They are frequently used in systems requiring speed or position control because they are easy to control and perform well. In this study, a DC servo system is modeled, state variables are obtained by using Kalman Filters in noisy and noiseless environments and optimal control with Linear Quadratic Regulator (LQR) is performed. In the study, mathematical model of the servo system was obtained and Matlab Simulink model was created. The LQR control of the system was then performed with state feedback. With this method, the design required for system control can be calculated optimally without the need for manipulation of the closed-loop poles. In addition, considering that it is not possible to measure all of the state variables in physical systems, the most accurate way to obtain the state variables of the system is an important factor for the control performance. Therefore, in the study, the state variables were obtained by Kalman state estimation. By applying process and measurement noise to the servo system, the performance of the controller in noisy and noiseless environments was compared with the classical methods. In this way, an algorithm with high stability has been developed which produces predictions of the actual unknown values with uncertainty by predicting the conditions and working with low error in noisy environments. According to the simulation results, the controller performance is satisfactory.", "keywords": [ "DC servo sistem, LQR, Kalman Filtresi, Matlab/Simulink, Optimal Kontrol" ] } ], "subjects": [], "subject_names": [], "published_date": "2019-06-01", "published_year": "2019", "received_date": null, "accepted_date": null, "volume": "4", "issue": null, "page_first": "572", "page_last": "578", "article_number": null, "conference": { "short_name": "ISAS2019-ENS", "full_title": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)", "name": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)", "acronym": "ISAS2019-ENS", "location": "Ankara, Türkiye", "start_date": "2019-04-19", "end_date": null, "public_id": "ed3b36e9-38cf-4aeb-a135-833c969cc89f" }, "proceedings": { "title": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)", "series_title": "Setsci conference indexing system", "publisher": "SET Technology", "isbn": null, "eisbn": null, "issn": "2618-6446", "eissn": null, "doi": null, "doi_prefix": null, "slug": "setsci-volume-4-2019-legacy-42", "public_id": "24d215ef-a545-4351-9725-6e9bdb167b3c", "url": "https://www.set-science.com/p/setsci-volume-4-2019-legacy-42" }, "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "code": "CC-BY-4.0", "open_access": true }, "copyright": { "holder": "SET Technology", "year": "2019" }, "funding_statement": null, "funders": [], "references": [ { "order": 1, "raw": "Deshpande P., Deshpande A., Inferential control of DC motor using Kalman Filter. 2nd International Conference on Power, Control and Embedded Systems 2012,IEEE. 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