scholarly journals Real-Time Aerodynamic Parameter Estimation Without Air Flow Angle Measurements

Author(s):  
Eugene Morelli
2021 ◽  
Vol 125 (1294) ◽  
pp. 2217-2228
Author(s):  
M. Mohamed ◽  
N. Joy

AbstractThis paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.


1991 ◽  
Vol 29 (1) ◽  
pp. 98-101 ◽  
Author(s):  
G. McCurrach ◽  
A. L. Evans ◽  
D. C. Smith ◽  
M. T. Gordon ◽  
M. B. D. Cooke

Author(s):  
Jin Wen ◽  
Theodore F. Smith

The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked more attention for energy efficient HVAC control and operation. Application of advanced control and operation strategies requires robust online system models. In this research, online models with parameter estimation for a building zone with variable air volume (VAV) system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and VAV entering air flow are modeled based on physical rules and using only the measurements that are commonly available in a commercial building. Different series of validation tests were performed in a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all the validation tests are less than 0.5°F for temperature outputs, and less than 50 ft3/min for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.


2018 ◽  
Vol 18 (1) ◽  
pp. 44-60 ◽  
Author(s):  
Stephen Sinclair

An adversarial autoencoder conditioned on known parameters of a physical modeling bowed string syn- thesizer is evaluated for use in parameter estimation and resynthesis tasks. Latent dimensions are provided to cap- ture variance not explained by the conditional parameters. Results are compared with and without the adversarial training, and a system capable of “copying” a given parameter-signal bidirectional relationship is examined. A real- -time synthesis system built on a generative, conditioned and regularized neural network is presented, allowing to construct engaging sound synthesizers based purely on recorded data. 


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