Zobrazeno 1 - 10
of 1 732
pro vyhledávání: '"forgetting factor"'
Publikováno v:
Journal of Applied Science and Engineering, Vol 28, Iss 6, Pp 1209-1218 (2024)
In order to minimize the effects of inner ambiguity and outer disturbance of the robotic arm model on the controlled system and to enhance the iterative performance, the paper designs an adaptive iterative control (AILC) method with forgetting factor
Externí odkaz:
https://doaj.org/article/bbd7480f4e644be0b62d5f5bc7977220
Publikováno v:
Guangtongxin yanjiu, Pp 23007601-23007606 (2024)
【Objective】With the development of the sixth generation mobile communication technology, the inter-carrier interference in the traditional Orthogonal Frequency Division Multiplexing (OFDM) system makes the channel estimation performance insuffici
Externí odkaz:
https://doaj.org/article/1ebde67d183843f49e0562a697d9666a
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 162, Iss , Pp 110329- (2024)
Current condition monitoring of three-phase inverters is mainly for DC-link capacitance, with little attention paid to AC-side parameters, and some of these methods require the addition of DC current sensors or changes in operating conditions. In fac
Externí odkaz:
https://doaj.org/article/2967730fac48471e88e1981fc0449c32
Autor:
Jianqiang Lin, Shing-Chow Chan
Publikováno v:
IEEE Access, Vol 12, Pp 138702-138715 (2024)
This paper proposes a new smoothly clipped absolute deviation (SCAD) regularized recursive identification algorithm for nonlinear Hammerstein systems having a finite duration impulse response (FIR) linear part in impulsive noise environment. It exten
Externí odkaz:
https://doaj.org/article/666df44a1bd543f2a8ee060f09e8920d
Publikováno v:
World Electric Vehicle Journal, Vol 15, Iss 9, p 399 (2024)
The sideslip angle and the yaw rate are the key state parameters for vehicle handling and stability control. To improve the accuracy of the input parameters and the time-varying characteristics of noise covariance in state estimation, a combined meth
Externí odkaz:
https://doaj.org/article/e96094e77b3c431e8be11e94eacb2c3f
Publikováno v:
Case Studies in Thermal Engineering, Vol 56, Iss , Pp 104239- (2024)
Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates sign
Externí odkaz:
https://doaj.org/article/864239418b9e46f8bab9660006bad00b
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In modern power systems, the extensive integration of renewable energy sources leads to a reduction in system inertia, thereby affecting grid stability. This study proposes a novel method for estimating grid inertia, which, under quasi-steady state c
Externí odkaz:
https://doaj.org/article/6d0323e207fa433fbd9e49803a8ff6e4
Autor:
Cui Kai
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In this paper, based on sampling and analysis of a large number of soft and weak sandwich slope data, several factors that have a great influence on slope stability are established, and a predictive analysis model describing the stability of soft and
Externí odkaz:
https://doaj.org/article/255af775904742fdab69e6b254ad178c
Publikováno v:
Energies, Vol 17, Iss 7, p 1640 (2024)
The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence a
Externí odkaz:
https://doaj.org/article/c969df350b4749518e50002325365983
Publikováno v:
IEEE Access, Vol 11, Pp 141152-141161 (2023)
Fast and accurate detections of state-of-health (SoH) are urgently required by various industrial sectors to facilitate reuse and recycling of Li-ion batteries. However, existing SoH identification methods rarely reconcile the monitoring speed and ac
Externí odkaz:
https://doaj.org/article/c2465244365f4bca969f09dbdb0a79ea