Zobrazeno 1 - 10
of 1 837
pro vyhledávání: '"neural network control"'
Autor:
Wentong Zhang, Bo Yu
Publikováno v:
Actuators, Vol 13, Iss 9, p 366 (2024)
An adaptive predefined-time quantized control issue is considered for strict-feedback systems with actuator quantization. To handle the unknown nonlinearities of a system, the neural networks are first applied to model them. To analyze the predefined
Externí odkaz:
https://doaj.org/article/ea0c0cf63ea544de8365b199f36286f6
Autor:
Suxia Wang, Yong Chen
Publikováno v:
Heliyon, Vol 10, Iss 5, Pp e26870- (2024)
In this paper, neural network control of fractional order chaotic systems (FOCSs) with input saturation and unknown sign of the controller gain is addressed by employing the Nussbaum function, where neural networks are utilized to model system uncert
Externí odkaz:
https://doaj.org/article/59ee8f46a85447559d34ebc517e8b62d
Autor:
Takialddin Al Smadi, Ahmed Handam, Khalaf S Gaeid, Adnan Al-Smadi, Yaseen Al-Husban, Al smadi Khalid
Publikováno v:
Results in Control and Optimization, Vol 14, Iss , Pp 100343- (2024)
Renewable energy systems, such as photovoltaic (PV) systems, have become increasingly significant in response to the pressing concerns of climate change and the imperative to mitigate carbon emissions. When static converters are used in solar power s
Externí odkaz:
https://doaj.org/article/a3052bd8b1644ecca6338024343fd95c
Publikováno v:
Mathematics, Vol 12, Iss 12, p 1880 (2024)
This study designed an adaptive neural network (NN) control method for a category of multi-robotic systems with parametric uncertainties. In practical engineering applications, systems commonly face design challenges due to uncertainties in their par
Externí odkaz:
https://doaj.org/article/cc70f4bbcb7b4d9cace91ce3fad62934
Publikováno v:
Results in Control and Optimization, Vol 13, Iss , Pp 100301- (2023)
This study addresses the challenge of speed control in permanent magnet synchronous motors (PMSMs), particularly in complex industrial applications. The research investigates the control of rotor speed in an electric-traction drive scenario. The setu
Externí odkaz:
https://doaj.org/article/8da8ee3d947f4d1fa078aaca58ba856a
Publikováno v:
Cogent Business & Management, Vol 10, Iss 1 (2023)
AbstractThe purpose of this study is to investigate the integration of forensic accounting and big data technology frameworks in relation to the mitigation of internal fraud risk in the banking industry. This study employed an explanatory research de
Externí odkaz:
https://doaj.org/article/4d94186a54aa4300b1a0ac42ddcd6bf5
Publikováno v:
工程科学学报, Vol 45, Iss 3, Pp 454-464 (2023)
With the vigorous development of material synthesis, mechanical manufacturing, and computer technology, as well as the in-depth study of control theory and bionics, robotics has undergone tremendous changes in recent decades. From rigid robots to dis
Externí odkaz:
https://doaj.org/article/a491767db400417fb79d1a1139d4760b
Publikováno v:
Agriculture, Vol 14, Iss 6, p 811 (2024)
In view of the typical requirements of large high-clearance sprayers, such as those operating in poor road conditions for farmland plant protection and at high operation speeds, reducing the vibration of sprayer suspension systems has become a resear
Externí odkaz:
https://doaj.org/article/4b123f0815e344ef8e551f46f4eea63c
Publikováno v:
Actuators, Vol 13, Iss 3, p 101 (2024)
Despite its excellent performance in path tracking control, the model predictive control (MPC) is limited by computational complexity in practical applications. The neural network control (NNC) is another attractive solution by learning the historica
Externí odkaz:
https://doaj.org/article/d1f22f3d93954b6ea29c9be67239f705
Publikováno v:
Frontiers in Computational Neuroscience, Vol 17 (2023)
This paper investigates the containment control of multiple unmanned surface vessels with nonlinear dynamics. To solve the leader-follower synchronization problem in a containment control system, a hierarchical control framework with a topology recon
Externí odkaz:
https://doaj.org/article/e45a1d07cfa64b86a65952f667b02ff5