Real-Time Predictive Flutter Analysis and Continuous Parameter Identification of Acclerating Aircraft

The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identification method was further integrated with an H-infinity filter (HIF)-based state estimator to monitor the SOC and capacity decay of the VRB in real-time. Experiments were carried out to validate the proposed method. The results suggested that the proposed method can achieve unbiased model parameter identification when unexpected noises corrupt the current and voltage measurements. SOC and capacity decay can also be estimated accurately in real-time without requiring additional open-circuit cells.

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Modified extended Kalman filtering and a real-time parallel algorithm for system parameter identification

IEEE Transactions on Automatic Control ◽

10.1109/9.45155 ◽

1990 ◽

Vol 35 (1) ◽

pp. 100-104 ◽

Cited By ~ 18

Author(s):

C.K. Chui ◽

G. Chen ◽

H.C. Chui

Keyword(s):

Parameter Identification ◽

Parallel Algorithm ◽

Real Time ◽

Kalman Filtering ◽

System Parameter ◽

Extended Kalman Filtering

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Synthesis of random parameter identification algorithms and estimates of current vessel motion parameters

E3S Web of Conferences ◽

10.1051/e3sconf/202016403004 ◽

2020 ◽

Vol 164 ◽

pp. 03004

Author(s):

Nikolay Ivanovskiy ◽

Ivan Gorychev ◽

Aleksandr Yashin ◽

Sergey Bidenko

Keyword(s):

Angular Velocity ◽

Parameter Identification ◽

Real Time ◽

Angular Position ◽

Random Parameter ◽

Moment Of Inertia ◽

Position Vector ◽

Random Parameters ◽

Motion Parameters ◽

Vessel Motion

The paper considers the task of synthesis of algorithms for identifying random parameters of a vessel, such as attached masses, moment of inertia, and estimating the current parameters of the vessel's motion from real-time measurements of onboard sensors. The task of the synthesis of algorithms for identifying random parameters of the vessel and evaluating the characteristics of the vessel’s movement is to determine (evaluate) the current parameters (attached masses, moment of inertia) and the characteristics of the vessel’s motion (position vector, speed) from the measurements of the vessel’s motion, angular position and angular velocity of the vessel rotation).

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