Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle
Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an a...
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| Autor*in: |
Heri Purnawan [verfasserIn] Ulul Ilmi [verfasserIn] Rifky Aisyatul Faroh [verfasserIn] Ahmad Bustanul Ali Ar Rizqi [verfasserIn] Fitroh Resmi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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In: InPrime - UIN Syarif Hidayatullah, 2023, 5(2023), 1, Seite 47-59 |
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| Übergeordnetes Werk: |
volume:5 ; year:2023 ; number:1 ; pages:47-59 |
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Link aufrufen |
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| DOI / URN: |
10.15408/inprime.v5i1.28313 |
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DOAJ091697530 |
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| 520 | |a Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 | ||
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10.15408/inprime.v5i1.28313 doi (DE-627)DOAJ091697530 (DE-599)DOAJ220bf34e6464468996bfd570e60d4cd2 DE-627 ger DE-627 rakwb eng QA1-939 Heri Purnawan verfasserin aut Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 autonomous vehicle kalman filter linear system mhe quadratic programming Mathematics Ulul Ilmi verfasserin aut Rifky Aisyatul Faroh verfasserin aut Ahmad Bustanul Ali Ar Rizqi verfasserin aut Fitroh Resmi verfasserin aut In InPrime UIN Syarif Hidayatullah, 2023 5(2023), 1, Seite 47-59 (DE-627)DOAJ090667271 27162478 nnns volume:5 year:2023 number:1 pages:47-59 https://doi.org/10.15408/inprime.v5i1.28313 kostenfrei https://doaj.org/article/220bf34e6464468996bfd570e60d4cd2 kostenfrei https://journal.uinjkt.ac.id/index.php/inprime/article/view/28313 kostenfrei https://doaj.org/toc/2686-5335 Journal toc kostenfrei https://doaj.org/toc/2716-2478 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 5 2023 1 47-59 |
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10.15408/inprime.v5i1.28313 doi (DE-627)DOAJ091697530 (DE-599)DOAJ220bf34e6464468996bfd570e60d4cd2 DE-627 ger DE-627 rakwb eng QA1-939 Heri Purnawan verfasserin aut Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 autonomous vehicle kalman filter linear system mhe quadratic programming Mathematics Ulul Ilmi verfasserin aut Rifky Aisyatul Faroh verfasserin aut Ahmad Bustanul Ali Ar Rizqi verfasserin aut Fitroh Resmi verfasserin aut In InPrime UIN Syarif Hidayatullah, 2023 5(2023), 1, Seite 47-59 (DE-627)DOAJ090667271 27162478 nnns volume:5 year:2023 number:1 pages:47-59 https://doi.org/10.15408/inprime.v5i1.28313 kostenfrei https://doaj.org/article/220bf34e6464468996bfd570e60d4cd2 kostenfrei https://journal.uinjkt.ac.id/index.php/inprime/article/view/28313 kostenfrei https://doaj.org/toc/2686-5335 Journal toc kostenfrei https://doaj.org/toc/2716-2478 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 5 2023 1 47-59 |
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10.15408/inprime.v5i1.28313 doi (DE-627)DOAJ091697530 (DE-599)DOAJ220bf34e6464468996bfd570e60d4cd2 DE-627 ger DE-627 rakwb eng QA1-939 Heri Purnawan verfasserin aut Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 autonomous vehicle kalman filter linear system mhe quadratic programming Mathematics Ulul Ilmi verfasserin aut Rifky Aisyatul Faroh verfasserin aut Ahmad Bustanul Ali Ar Rizqi verfasserin aut Fitroh Resmi verfasserin aut In InPrime UIN Syarif Hidayatullah, 2023 5(2023), 1, Seite 47-59 (DE-627)DOAJ090667271 27162478 nnns volume:5 year:2023 number:1 pages:47-59 https://doi.org/10.15408/inprime.v5i1.28313 kostenfrei https://doaj.org/article/220bf34e6464468996bfd570e60d4cd2 kostenfrei https://journal.uinjkt.ac.id/index.php/inprime/article/view/28313 kostenfrei https://doaj.org/toc/2686-5335 Journal toc kostenfrei https://doaj.org/toc/2716-2478 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 5 2023 1 47-59 |
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10.15408/inprime.v5i1.28313 doi (DE-627)DOAJ091697530 (DE-599)DOAJ220bf34e6464468996bfd570e60d4cd2 DE-627 ger DE-627 rakwb eng QA1-939 Heri Purnawan verfasserin aut Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 autonomous vehicle kalman filter linear system mhe quadratic programming Mathematics Ulul Ilmi verfasserin aut Rifky Aisyatul Faroh verfasserin aut Ahmad Bustanul Ali Ar Rizqi verfasserin aut Fitroh Resmi verfasserin aut In InPrime UIN Syarif Hidayatullah, 2023 5(2023), 1, Seite 47-59 (DE-627)DOAJ090667271 27162478 nnns volume:5 year:2023 number:1 pages:47-59 https://doi.org/10.15408/inprime.v5i1.28313 kostenfrei https://doaj.org/article/220bf34e6464468996bfd570e60d4cd2 kostenfrei https://journal.uinjkt.ac.id/index.php/inprime/article/view/28313 kostenfrei https://doaj.org/toc/2686-5335 Journal toc kostenfrei https://doaj.org/toc/2716-2478 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 5 2023 1 47-59 |
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10.15408/inprime.v5i1.28313 doi (DE-627)DOAJ091697530 (DE-599)DOAJ220bf34e6464468996bfd570e60d4cd2 DE-627 ger DE-627 rakwb eng QA1-939 Heri Purnawan verfasserin aut Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 autonomous vehicle kalman filter linear system mhe quadratic programming Mathematics Ulul Ilmi verfasserin aut Rifky Aisyatul Faroh verfasserin aut Ahmad Bustanul Ali Ar Rizqi verfasserin aut Fitroh Resmi verfasserin aut In InPrime UIN Syarif Hidayatullah, 2023 5(2023), 1, Seite 47-59 (DE-627)DOAJ090667271 27162478 nnns volume:5 year:2023 number:1 pages:47-59 https://doi.org/10.15408/inprime.v5i1.28313 kostenfrei https://doaj.org/article/220bf34e6464468996bfd570e60d4cd2 kostenfrei https://journal.uinjkt.ac.id/index.php/inprime/article/view/28313 kostenfrei https://doaj.org/toc/2686-5335 Journal toc kostenfrei https://doaj.org/toc/2716-2478 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 5 2023 1 47-59 |
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To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. 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Moving Horizon State Estimation for Linear System with Application to Autonomous Vehicle |
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Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 |
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Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 |
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Abstract This paper proposes moving horizon estimation (MHE) to estimate the state variables of autonomous vehicle linear systems under measurement noises. To solve the MHE optimization problem, quadratic programming is employed. The steering angle, yaw angle, and global position constraints of an autonomous vehicle are considered in the estimation design. According to the simulation results, it can be observed that although the longer MHE step can give better results compared to the shorter MHE step, the difference in the MHE step only slightly affects the estimated results. However, the longer MHE step can increase the computational time. Additionally, the proposed MHE scheme is compared to the Kalman filter (KF) estimator. Based on the obtained results, the KF gives a better estimation than the MHE, but this notion must be verified for other case studies. Keywords: autonomous vehicle; Kalman filter; linear system; MHE; quadratic programming. Abstrak Paper ini mengusulkan moving horizon estimation (MHE) untuk mengestimasi variabel keadaan sistem linier kendaraan otonom karena pengaruh noise pengukuran. Untuk menyelesaikan masalah optimasi MHE, digunakan pemrograman kuadratik. Kendala sudut kemudi, sudut yaw dan posisi global dari kendaraan otonom dipertimbangkan dalam desain estimasi. Dari hasil simulasi dapat diketahui bahwa meskipun langkah MHE yang lebih panjang dapat memberikan hasil yang lebih baik dibandingkan dengan langkah MHE yang lebih pendek, perbedaan langkah MHE hanya sedikit mempengaruhi hasil estimasi. Namun, langkah MHE yang semakin panjang dapat meningkatkan waktu komputasi. Selain itu, skema MHE yang diusulkan dibandingkan dengan estimator Kalman filter (KF). Berdasarkan hasil yang diperoleh, KF memberikan estimasi yang lebih baik daripada MHE, tetapi gagasan ini harus diverifikasi untuk studi kasus lainnya. Kata Kunci: kendaraan otonom; Kalman filter; sistem linier; MHE; pemrograman kuadratik. 2020MSC: 62P35, 65D19 |
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