Gaussian Process-Based Model Predictive Control for Autonomous Underwater Vehicles

التفاصيل البيبلوغرافية
العنوان:	Gaussian Process-Based Model Predictive Control for Autonomous Underwater Vehicles
المؤلفون:	Xuyu, Shen, Gongwu, Sun, Ying, Mao, Xuanyu, Hu, Zhenzhong, Chu
المصدر:	Journal of Physics: Conference Series ; volume 2718, issue 1, page 012063 ; ISSN 1742-6588 1742-6596
بيانات النشر:	IOP Publishing
سنة النشر:	2024
الوصف:	Traditional MPC algorithms, assuming constant values, suffer from performance degradation caused by model mismatch. This paper addresses the enhancement of predictability in Autonomous Underwater Vehicles (AUVs) under uncertain disturbances and unknown system dynamics through the design of Model Predictive Controllers (MPCs). We propose a hybrid model that integrates a first-principles nominal model with a learning-based model utilizing Gaussian Processes (GPs). The algorithm addresses the problem of model mismatch in AUV motion control by constructing a precise GP model, which captures the dynamic characteristics of the process through the collection and learning of deviations between the reference model and the controlled system. Additionally, the GP model transforms stochastic constraints into deterministic convex constraints, enhancing safety guarantees in complex and challenging environments. The effectiveness of the proposed algorithm is demonstrated through two simulation examples.
نوع الوثيقة:	article in journal/newspaper
اللغة:	unknown
DOI:	10.1088/1742-6596/2718/1/012063
DOI:	10.1088/1742-6596/2718/1/012063/pdf
الاتاحة:	http://dx.doi.org/10.1088/1742-6596/2718/1/012063 https://iopscience.iop.org/article/10.1088/1742-6596/2718/1/012063 https://iopscience.iop.org/article/10.1088/1742-6596/2718/1/012063/pdf
Rights:	http://creativecommons.org/licenses/by/3.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining
رقم الانضمام:	edsbas.9B59023A
قاعدة البيانات:	BASE

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الوصف
DOI:	10.1088/1742-6596/2718/1/012063