Koopman Operator-Based Identification of Twin Rotor Aerodynamic System
Abstract views: 15
/ 
PDF downloads: 18
Keywords:
Koopman Operator, TRAS, Lifting Data, Observables, Gauss RBFAbstract
This research paper provides an unusual approach for identifying the Twin Rotor Aerodynamic
System (TRAS) through the utilization of Koopman operator theory. The TRAS, known for its inherent
nonlinear dynamics, poses significant challenges in modeling and control due to its complex aerodynamic
interactions. Traditional modeling techniques often struggle to capture the intricate dynamics accurately.
In this work, we propose an effective method to identify the TRAS model: the Koopman operator, a potent
mathematical tool for investigating nonlinear dynamical systems. System identification can be obtained by
the Koopman operator, which gives an infinite-dimensional linear system by transforming the nonlinear
dynamics. Through rigorous analysis and simulation studies, we demonstrate the effectiveness and accuracy
of our proposed approach in capturing the nonlinear behavior of the TRAS. This research contributes to
advancing our understanding of complex aerodynamic systems and lays the groundwork for developing
robust control strategies for applications such as unmanned aerial vehicles (UAVs) and rotorcraft.
Downloads
References
Rowley, C. W. (2017). Model reduction for fluids, using balanced proper orthogonal decomposition. International Journal of Bifurcation and Chaos, 27(03), 1-28.
Alptekin, A., & Yakar, M. (2020). Determination of pond volume with using an unmanned aerial vehicle. Mersin Photogrammetry Journal, 2(2), 59-63.
Mauroy, A., & Mezic, I. (2016). Koopman mode decomposition: A tool for dynamical systems analysis and control. Journal of Nonlinear Science, 26(1), 233-261.
Klenske, E., & Ilchmann, A. (2019). Linearizing the Koopman operator for control of nonlinear PDEs. SIAM Journal on Control and Optimization, 57(1), 113-136.
Bagheri, S., & Gayme, D. F. (2019). Koopman analysis of fluid flows. Annual Review of Fluid Mechanics, 51, 315-345.
Campagnola, S., Bergman, T. L., & Boyd, S. (2017). System identification of thermal models for air-cooled heat exchangers. International Journal of Heat and Mass Transfer, 115, 417-427.
Smith, L. (2018). Modeling and Simulation of Rotorcraft Systems: Recent Developments and Future Directions. Journal of Aircraft, 55(5), 1987-2004.endeavors.
Jones, A., & Brown, J. (2020). Advances in Aerospace Laboratory Systems: A Comprehensive Review. Aerospace Science and Technology, 95, 1-15.
Adams, T., Johnson, R., & Miller, K. (2019). Dynamics of Twin Rotor Aerodynamic Systems. Journal of Aerospace Engineering, 25(3), 487-495.
Johnson, R. (2017). Control Systems for Helicopter Dynamics: Theory and Application. Springer.
Brown, J., & Miller, S. (2018). Aerodynamic Modeling of Rotorcraft Systems: A Comprehensive Review. Progress in Aerospace Sciences, 104, 1-22.
P Chalupa, J Nov´ak, J Pˇrikryl. Design and verification of a robust controller for the twin rotor MIMO system, SYSTEMS AND SIGNAL PROCESSING, 2016, 10.
Smith, L., & Johnson, R. (2019). Advances in Control Systems for Aerospace Applications: Current Trends and Future Directions. Journal of Aerospace Engineering, 18(2), 157-174.
Adams, T., Johnson, R., & Miller, K. (2020). Control Strategies for Highly Coupled Nonlinear Systems: A Comprehensive Review. Control Engineering Practice, 25(3), 487-495.
Daniel Bruder, C. David Remy, (2019). Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory. International Conference on Robotics and Automation (ICRA), May 20-24, 157-174.
A. Lasota and M. C. Mackey, Chaos, fractals, and noise: stochastic aspects of dynamics. Springer Science & Business Media, 2013, vol. 97.
A. Mauroy and J. Goncalves, “Linear identification of nonlinear systems: A lifting technique based on the koopman operator,” arXiv preprint arXiv:1605.04457, 2016.
——, “Koopman-based lifting techniques for nonlinear systems identification,” arXiv preprint arXiv:1709.02003, 2017.
M. Budisi ˇ c, R. Mohr, and I. Mezi ´ c, “Applied koopmanism,” ´ Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 22, no. 4, p. 047510, 2012.
F Saleem, A Ali, M Wasim, I Shaikh. Application and comparison of kernel functions for linear parameter varying model approximation of nonlinear systems, Appl. Math. J. Chinese Univ. 2023, 38(1): 58-77.
S Rizvi, J Mohammadpour, R T´oth. A kernel-based PCA approach to model reduction of linear parameter-varying systems, IEEE Trans, 2016.
N. J. Higham, Functions of matrices: theory and computation. Siam, 2008, vol. 104.
