More Like this

1. System Identification Flight Testing of Inverted V-Tail Small Unmanned Air System

   https://doi.org/10.2514/6.2022-2408  

   Christopher Leshikar, Scott Gosnell (January 2022, AIAA SCITECH 2022 Forum)

   This paper presents an approach for generating linear time invariant state-space models of a small Unmanned Air System. An instrumentation system using the robot operating system with commercial-off-the-shelf components is implemented to record flight data and inject auto- mated excitation signals. Offline system identification is conducted using the Observer/Kalman Identification algorithm to produce a discrete-time linear time invariant state-space model, which is then converted to a continuous time-model for analysis. Challenges concerning data collection and inverted V-Tail modelling are discussed, and solutions are presented. Longitudiunal, lateral/directional and combined longitudinal lateral/directional models of the test vehicle are generated using both manual and automated excitations, and are presented and compared. The generated longitudinal and lateral/directional results are compared to results for a small Unmanned Air System with a standard empennage. Flight test results presented in the paper show decent matching between the decoupled longitudinal and lateral/directional model and the combined longitudinal/lateral directional model. 

   more » « less
2. System 1 + System 2 = Better World: Neural-Symbolic Chain of Logic Reasoning

   Hua, Wenyue; Zhang, Yongfeng (December 2022, Findings of the Association for Computational Linguistics: EMNLP 2022)
3. Timing system of LIGO discoveries

   https://doi.org/10.1103/PhysRevD.108.022003  

   Sullivan, Andrew G; Asali, Yasmeen; Márka, Zsuzsanna; Sigg, Daniel; Countryman, Stefan; Bartos, Imre; Kawabe, Keita; Pirello, Marc D; Thomas, Michael; Shaffer, Thomas J; et al (July 2023, Physical Review D)
4. Variational System Identification of Aircraft

   Dutra, Dimas A (January 2025, AIAA journal)

   Variational system identification is a new formulation of maximum likelihood for estimation of parameters of dynamical systems subject to process and measurement noise, such as aircraft flying in turbulence. This formulation is an alternative to the filter-error method that circumvents the solution of a Riccati equation and does not have problems with unstable predictors. In this paper, variational system identification is demonstrated for estimating aircraft parameters from real flight-test data. The results show that, in real applications of practical interest, it has better convergence properties than the filter-error method, reaching the optimum even when null initial guesses are used for all parameters and decision variables. This paper also presents the theory behind the method and practical recommendations for its use. 

   more » « less
5. Airfoil Oscillator System: Data-Driven System Identification

   https://doi.org/10.2514/6.2025-2506  

   Nemani, Nishant; Balachandran, Balakumar (January 2025, American Institute of Aeronautics and Astronautics)

   With the computational resources becoming available, data-driven methods have emerged as powerful means for equation discovery and model construction. Sparse regression methods such as SINDy (Sparse Identification for Nonlinear Dynamical Systems) can be used for developing reduced-order models of nonlinear systems. In this study, the authors examine how SINDy can be used for developing low-dimensional models for airfoil systems, which experience unsteady aerodynamic loads and flutter instabilities. For a system of multiple closely spaced airfoil oscillators, analytical models are not readily available to determine flutter instabilities, and one has to take recourse to experimental and numerical means. In this work, as a starting point, data collected through simulations of unsteady aerodynamics of a single airfoil oscillator system are considered and a reduced-order model is constructed based on this data. 

   more » « less