January  2015, 11(1): 231-240. doi: 10.3934/jimo.2015.11.231

Control augmentation design of UAVs based on deviation modification of aerodynamic focus

1. 

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu

2. 

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China, China

Received  February 2013 Revised  January 2014 Published  May 2014

Based on the analysis of force and action points of an UAV (unmanned aerial vehicle), we propose a concept called static stability degree deviation (SSDD) factor, which is related to the focus position, and can be used to modify the data for control law design. Furthermore, a SSDD-based method is presented to avoid the flight oscillation caused by the data deviation of aerodynamic focus. By using the attitude angle difference between real fight data and simulation data as an optimization index, the identification of SSDD factor and the data reproduction of the real flight data are achieved. The identification results are then used to modify aerodynamic blowing data. Based on the modified model, the augmentation control is designed by applying the altitude angle rate feedback to improve the equivalent damping ratio and frequency; thus the iteration design of the control law is performed.
Citation: Yingjing Shi, Rui Li, Honglei Xu. Control augmentation design of UAVs based on deviation modification of aerodynamic focus. Journal of Industrial & Management Optimization, 2015, 11 (1) : 231-240. doi: 10.3934/jimo.2015.11.231
References:
[1]

Y. A. Hwang and Y. H. Liao, Reduction and dynamic approach for the multi-choice Shapley value,, Journal of Industrial and Management Optimization, 9 (2013), 885. doi: 10.3934/jimo.2013.9.885. Google Scholar

[2]

H. J. Lin and T. S. Tsay, Modeling identification and simulation of bank to turn unmanned aerial vehicle,, Wseas Transactions on Systems, 10 (2011), 91. Google Scholar

[3]

C. P. Liu and H. W. Lee, Identification for systems governed by nonlinear interval differential equations,, Journal of Industrial and Management Optimization, 8 (2012), 765. doi: 10.3934/jimo.2012.8.765. Google Scholar

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W. C. Luo and L. Chen, Approximation schemes for scheduling a maintenance and linear deteriorating jobs,, Journal of Industrial and Management Optimization, 8 (2012), 271. doi: 10.3934/jimo.2012.8.271. Google Scholar

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S. E. Lyshevski, Identification of nonlinear flight dynamics: Theory and practice,, IEEE Transactions on Aerospace and Electronic Systems, 36 (2000), 383. doi: 10.1109/7.845215. Google Scholar

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L. Mevel, A. Benveniste, M. Basseville and M. Goursat, Input-output versus output-only data processing for structural identification-Application in flight data analysis,, Journal of Sound and Vibration, 295 (2006), 531. doi: 10.1016/j.jsv.2006.01.039. Google Scholar

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Y. L. Nong, Z. K. Qi and D. F. Lin, System identification of a small unmanned aerial vehicle based on time and frequency domain technologies,, Proceedings of the 8th World Congress on Intelligent Control and Automation, (2011), 711. Google Scholar

[8]

J. O. Pedro and P. Kantue, Online aerodynamic parameter estimation of a miniature unmanned helicopter using radial basis function neural networks,, Proceedings 8th Asian Control Conference (ASCC), (2011), 1170. Google Scholar

[9]

W. Qing, K. Y. Wu, T. J. Zhang, Y. N. Kong and W. Q. Qian, Aerodynamic modeling and parameter estimation from qar data of an airplane approaching a high-altitude airport,, Chinese Journal of Aeronautics, 25 (2012), 361. Google Scholar

[10]

S. A. Salman, V. R. Puttige and S. G. Anavatti, Real-time validation and comparison of fuzzy identification and state-space identification for a uav platform,, International Conference on Control Applications, (2006), 2138. Google Scholar

[11]

S. A. Salman, A. G. Sreenatha and J. Y. Choi, Attitude dynamics identification of unmanned aircraft vehicle,, International Journal of Control, 4 (2006), 782. Google Scholar

[12]

M. K. Samal, S. Anavatti and M. Garratt, Neural network based system identification for autonomous flight of an eagle helicopter,, Proceedings of the 17th World Congress The International Federation of Automatic Control, (2008), 7421. Google Scholar

[13]

Y. J. Shi, Control and Simulation of Aviation Aircrafts,, 1nd edition, (2011). Google Scholar

[14]

Z. K. Shi and F. X. Wu, Robust identification method for nonlinear model structures and its application to high-performance aircraft,, International Journal of Systems Science, 44 (2013), 1040. doi: 10.1080/00207721.2011.652225. Google Scholar

[15]

J. Suk, Y. Lee and S. Kim, System identification and stability evaluation of an unmanned aerial vehicle from automated flight tests,, KSME International Journal, 17 (2003), 654. Google Scholar

[16]

F. A. Viana and B. C. Maciel, Aircraft longitudinal stability and control derivatives identification by using life cycle and Levenberg-Marquardt optimization algorithms,, Inverse Problems in Science and Engineering, 17 (2009), 17. Google Scholar

show all references

References:
[1]

Y. A. Hwang and Y. H. Liao, Reduction and dynamic approach for the multi-choice Shapley value,, Journal of Industrial and Management Optimization, 9 (2013), 885. doi: 10.3934/jimo.2013.9.885. Google Scholar

[2]

H. J. Lin and T. S. Tsay, Modeling identification and simulation of bank to turn unmanned aerial vehicle,, Wseas Transactions on Systems, 10 (2011), 91. Google Scholar

[3]

C. P. Liu and H. W. Lee, Identification for systems governed by nonlinear interval differential equations,, Journal of Industrial and Management Optimization, 8 (2012), 765. doi: 10.3934/jimo.2012.8.765. Google Scholar

[4]

W. C. Luo and L. Chen, Approximation schemes for scheduling a maintenance and linear deteriorating jobs,, Journal of Industrial and Management Optimization, 8 (2012), 271. doi: 10.3934/jimo.2012.8.271. Google Scholar

[5]

S. E. Lyshevski, Identification of nonlinear flight dynamics: Theory and practice,, IEEE Transactions on Aerospace and Electronic Systems, 36 (2000), 383. doi: 10.1109/7.845215. Google Scholar

[6]

L. Mevel, A. Benveniste, M. Basseville and M. Goursat, Input-output versus output-only data processing for structural identification-Application in flight data analysis,, Journal of Sound and Vibration, 295 (2006), 531. doi: 10.1016/j.jsv.2006.01.039. Google Scholar

[7]

Y. L. Nong, Z. K. Qi and D. F. Lin, System identification of a small unmanned aerial vehicle based on time and frequency domain technologies,, Proceedings of the 8th World Congress on Intelligent Control and Automation, (2011), 711. Google Scholar

[8]

J. O. Pedro and P. Kantue, Online aerodynamic parameter estimation of a miniature unmanned helicopter using radial basis function neural networks,, Proceedings 8th Asian Control Conference (ASCC), (2011), 1170. Google Scholar

[9]

W. Qing, K. Y. Wu, T. J. Zhang, Y. N. Kong and W. Q. Qian, Aerodynamic modeling and parameter estimation from qar data of an airplane approaching a high-altitude airport,, Chinese Journal of Aeronautics, 25 (2012), 361. Google Scholar

[10]

S. A. Salman, V. R. Puttige and S. G. Anavatti, Real-time validation and comparison of fuzzy identification and state-space identification for a uav platform,, International Conference on Control Applications, (2006), 2138. Google Scholar

[11]

S. A. Salman, A. G. Sreenatha and J. Y. Choi, Attitude dynamics identification of unmanned aircraft vehicle,, International Journal of Control, 4 (2006), 782. Google Scholar

[12]

M. K. Samal, S. Anavatti and M. Garratt, Neural network based system identification for autonomous flight of an eagle helicopter,, Proceedings of the 17th World Congress The International Federation of Automatic Control, (2008), 7421. Google Scholar

[13]

Y. J. Shi, Control and Simulation of Aviation Aircrafts,, 1nd edition, (2011). Google Scholar

[14]

Z. K. Shi and F. X. Wu, Robust identification method for nonlinear model structures and its application to high-performance aircraft,, International Journal of Systems Science, 44 (2013), 1040. doi: 10.1080/00207721.2011.652225. Google Scholar

[15]

J. Suk, Y. Lee and S. Kim, System identification and stability evaluation of an unmanned aerial vehicle from automated flight tests,, KSME International Journal, 17 (2003), 654. Google Scholar

[16]

F. A. Viana and B. C. Maciel, Aircraft longitudinal stability and control derivatives identification by using life cycle and Levenberg-Marquardt optimization algorithms,, Inverse Problems in Science and Engineering, 17 (2009), 17. Google Scholar

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