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Artificial bee colony optimised controller for small-scale unmanned helicopter

Part of: APISAT 2015

Published online by Cambridge University Press:  03 November 2017

R. Ma ,
H. Wu  and
L. Ding
R. Ma*
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
H. Wu
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
L. Ding
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
*
marui@nuaa.edu.cn
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Abstract

In this paper, an efficient approach to design and optimize a flight controller of a small-scale unmanned helicopter is proposed. Given the identified helicopter model, the Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) robust control method is applied for trajectory tracking and attitude control of the helicopter with a two-loop hierarchical control architecture. Since the performance of the controller extremely depends on its weighting matrices, the Artificial Bee Colony (ABC) algorithm is introduced to automatically select the parameters of the matrices. Comparative studies between optimal algorithms are also carried out. A series of flight experiments and simulations are conducted to investigate the effectiveness and robustness of the proposed optimised controller.

Keywords

Small-scale unmanned helicopterLQG/LTRartificial bee colony algorithmflight experimentstrajectory tracking
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1246 , December 2017 , pp. 1879 - 1896
Copyright
Copyright © Royal Aeronautical Society 2017 

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References

REFERENCES

1. Michael, N., Mellinger, D., Lindsey, Q. and Kumar, V. The grasp multiple micro-UAV testbed, IEEE Robotics & Automation Magazine, 2010, 17, (3), pp 56–65.Google Scholar
2. Maza, I., Kondak, K., Bernard, M. and Ollero, A. Multi-UAV Cooperation and Control for Load Transportation and Deployment. Journal of Intelligent and Robotic Systems, 2010, 57, (1-4), 417. DOI: https://doi.org/10.1007/s10846-009-9352-8 Google Scholar
3. Raptis, I.A., Valavanis, K.P. and Vachtsevanos, G.J. Linear tracking control for small-scale unmanned helicopters, IEEE Transactions on Control Systems Technology, 2012, 20, (4), pp 995–1010.Google Scholar
4. Tang, S., Lu, X. and Zheng, Z. Platform and state estimation design of a small-scale UAV helicopter system, Int J Aerospace Engineering, 2013, 2013.Google Scholar
5. Cai, G., Wang, B., Chen, B.M. and Lee, T.H. Design and implementation of a flight control system for an unmanned rotorcraft using RPT control approach, Asian J Control, 15, (1), pp 95–119.Google Scholar
6. Shin, J., Nonami, K., Fujiwara, D. and Hazawa, K. Model-based optimal attitude and positioning control of small-scale unmanned helicopter, Robotica, 2005, 23, (1), pp 51–63.CrossRefGoogle Scholar
7. Mori, R., Hirata, K., Tamaki, T. and Yonezawa, N. Vision-based guidance control of a small-scale unmanned helicopter, J of the Robotics Society of Japan, 2008, 26, (8), pp 905–912.Google Scholar
8. Stein, G. and Athans, M. The LQG/LTR procedure for multivariable feedback control design, IEEE Transactions on Automatic Control, 1987, 32, (2), pp 105–114.Google Scholar
9. Wise, K. A. A trade study on missile autopilot design using optimal control theory, AIAA Guidance, Navigation and Control Conference, 2007, American Institute of Aeronautics and Astronautics, Hilton Head, South Carolina. pp 2007-6673.Google Scholar
10. Zarei, J., Montazeri, A., Motlagh, M.R.J. and Poshtan, J. Design and comparison of LQG/LTR and H controllers for a VSTOL flight control system, J of the Franklin Institute, 2007, 344, (5), pp 577–594.CrossRefGoogle Scholar
11. Barrera-Cardenas, R. and Molinas, M. Optimal LQG controller for variable speed wind turbine based on genetic algorithms, Energy Procedia, 2012, 20, pp 207–216.CrossRefGoogle Scholar
12. Zhang, M., Sun, P., Cao, R. and Zhu, J. LQG/LTR flight controller optimal design based on differential evolution algorithm, 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA), May 2010, volume 2, IEEE, Changsha, China, pp 613-616.Google Scholar
13. Mei, T.X. and Goodall, R.M. LQG and GA solutions for active steering of railway vehicles, IEE Proceedings-Control Theory and Applications, 2000, 147, (1), pp 111–117.Google Scholar
14. Da Fonseca Neto, J.V., Abreu, I.S. and Da Silva, F.N., NeuralGenetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40, (2), pp 266–285.Google Scholar
15. Karaboga, D. and Basturk, B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization, 2007, 39, (3), pp 459–471.CrossRefGoogle Scholar
16. Karaboga, D. and Basturk, B. On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 2008, 8, (1), pp 687–697.Google Scholar
17. Abachizadeh, M., Yazdi, M.R.H. and Yousefi-Koma, A. Optimal tuning of PID controllers using artificial bee colony algorithm, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), July 2010, IEEE, Montreal, QC, Canada, pp 379-384.Google Scholar
18. Changhao, S. and Duan, H. Artificial bee colony optimized controller for unmanned rotorcraft pendulum, Aircraft Engineering and Aerospace Technology, 2013, 85, (2), pp 104–114.CrossRefGoogle Scholar
19. Abedinia, O., Wyns, B. and Ghasemi, A. Robust fuzzy PSS design using ABC, 2011 10th International Conference on Environment and Electrical Engineering (EEEIC), May 2011, IEEE, Rome, Italy, pp 1-4.Google Scholar
20. Mettler, B. Identification Modeling and Characteristics of Miniature Rotorcraft, Springer Science & Business Media, New York, US, 2013.Google Scholar
21. Mettler, B., Tischler, M.B., Kanade, T. and Messner, W. Attitude control optimization for a small-scale unmanned helicopter, AIAA Guidance, Navigation, and Control Conference, August 2000, Denver, CO, US, pp 40-59.CrossRefGoogle Scholar
22. Wang, J. and Hu, X. The Application of MATLAB in Vibration Signal Processing. China Water & Power Press, Beijing, 2006.Google Scholar
23. Shim, D.H., Kim, H.J. and Sastry, S. Control system design for rotorcraft-based unmanned aerial vehicles using time-domain system identification, Proceedings of the 2000 IEEE International Conference on Control Applications, 2000, IEEE, Anchorage, Alaska, US, pp 808-813.Google Scholar
24. Kim, H.C., Dharmayanda, H.R., Kang, T., Budiyono, A., Lee, G. and Adiprawita, W. Parameter identification and design of a robust attitude controller using H methodology for the raptor E620 small-scale helicopter, Int J Control, Automation and Systems, 2012, 10, (1), pp 88–101.Google Scholar
25. Cai, G., Chen, B.M. and Lee, T.H. Unmanned Rotorcraft Systems, Springer Science & Business Media, Springer, London, 2011.CrossRefGoogle Scholar
26. Wang, B., Chen, B.M. and Lee, T.H. An RPT approach to time-critical path following of an unmanned helicopter, 2011 8th Asian Control Conference (ASCC), May 2011, IEEE, Kaohsiung, Taiwan, pp 211-216.Google Scholar