Zobrazeno 1 - 10
of 1 216
pro vyhledávání: '"learning and control"'
Publikováno v:
AgriEngineering, Vol 6, Iss 2, Pp 1417-1435 (2024)
This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environm
Externí odkaz:
https://doaj.org/article/7f22328899494955933e6e3f8c7689c1
Publikováno v:
In Ocean Engineering 15 February 2025 318
Autor:
Kim, Dong-Hwi a, Kim, Moon Hwan b, Kim, Jun a, Baek, Hyung-Min a, Choi, Young-Myung a, c, Shin, Sung-chul a, c, Kim, Minwoo d, Kim, Yagin c, Kim, Eun Soo a, c, ⁎, Lee, Seung Hwan e, ⁎⁎
Publikováno v:
In Ocean Engineering 15 February 2025 318
Publikováno v:
In Acta Astronautica February 2025 227:57-66
Autor:
Nadezhda Kunicina, Vladimir Beliaev, Roberts Grants, Jelena Caiko, Raikhan Amanova, Rasa Brūzgienė, Madina Mansurova
Publikováno v:
Applied Sciences, Vol 14, Iss 23, p 11150 (2024)
With the global shift to electric vehicles, countries face unique challenges and opportunities shaped by their geographical and economic contexts. This paper presents a system that leverages smart transport technologies, the Internet of Things, and d
Externí odkaz:
https://doaj.org/article/8b6fd09694c34afcbf64b3a095d4240d
Publikováno v:
IEEE Open Journal of Control Systems, Vol 3, Pp 375-388 (2024)
This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. A key challenge in designing controllers with stability guarantees for uncertain
Externí odkaz:
https://doaj.org/article/02e405ca43c3491c98921cc92745dedf
Publikováno v:
IEEE Open Journal of Control Systems, Vol 3, Pp 342-357 (2024)
The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms.
Externí odkaz:
https://doaj.org/article/71bd8993ce954cd19a7aa25a2ed76f9d
Publikováno v:
IEEE Access, Vol 12, Pp 57410-57423 (2024)
This paper presents a novel deep learning framework for robotic path planning that seamlessly integrates Linear Temporal Logic (LTL) with trajectory optimization to meet mission specifications efficiently. Our approach innovates on several fronts: Fi
Externí odkaz:
https://doaj.org/article/cc2f7cfae7b549ff9a0557428bbbb508
Autor:
Kyoungho Lee, Kyunghoon Cho
Publikováno v:
IEEE Access, Vol 12, Pp 7704-7718 (2024)
This paper introduces an innovative deep learning framework for robotic path planning. This framework addresses two fundamental challenges: (1) integration of mission specifications defined through Linear Temporal Logic (LTL), and (2) enhancement of
Externí odkaz:
https://doaj.org/article/32613d4641e64c20b5d30f953fce4d22
Publikováno v:
SICE Journal of Control, Measurement, and System Integration, Vol 16, Iss 1, Pp 349-362 (2023)
In many practical control applications, the performance level of a closed-loop system degrades over time due to the change of plant characteristics. Thus, there is a strong need for redesigning a controller without going through the system modelling
Externí odkaz:
https://doaj.org/article/fb4ac3311e67405ab87d667b9356413f