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Neural network formation control of a team of tractor–trailer systems

Published online by Cambridge University Press:  03 April 2017

Khoshnam Shojaei
Khoshnam Shojaei*
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper addresses the formation tracking control of a group of tractor–trailer systems in the presence of model uncertainties. A virtual leader–follower formation technique is used to design a controller in order to force a team of tractor–trailer systems to construct a desired formation configuration. Since tractor–trailer systems have a nonlinear multi-input multi-output model with strong couplings, multi-layer neural networks are employed to overcome unknown nonlinearities and uncertain parameters by using on-line weight tuning algorithms. Neural network approximation errors and external disturbances are also compensated with adaptive robust signals. The dynamic surface control approach has been used to reduce the complexity of the proposed controller effectively. Lyapunov’s direct method proves that all signals in the closed-loop formation control system are bounded and tracking errors converge to a neighborhood of the origin whose size is adjustable. Finally, simulation results will be provided to illustrate the efficiency of the proposed controller.

Keywords

Adaptive controlDynamic surface controlFormation controlNeural networkTractor–trailerVirtual leader–follower
Type
Articles
Information
Robotica , Volume 36 , Issue 1 , January 2018 , pp. 39 - 56
Copyright
Copyright © Cambridge University Press 2017 

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References

1. Balch, T. and Arkin, R. C., “Behavior-based formation control for multirobot teams,” IEEE Trans. Robot. Autom. 14 (6), 926–939 (1998).Google Scholar
2. Fredslund, J. and Mataric, M. J., “A general algorithm for robot formations using local sensing and minimal communication,” IEEE Trans. Robot. Autom. 18 (5), 837–846 (2002).Google Scholar
3. Lewis, M. A. and Tan, K.-H., “High precision formation control of mobile robots using virtual structures,” Auton. Robots 4 (4), 387–403 (1997).Google Scholar
4. Do, K. D., “Formation tracking control of unicycle-type mobile robots with limited sensing ranges,” IEEE Trans. Control Syst. Technol. 16 (3), 527–538 (2008).Google Scholar
5. Consolini, L., Morbidi, F., Prattichizzo, D. et al., “Leader and follower formation control of nonholonomic mobile robots with input constraints,” Automatica 44 (5), 1343–1349 (2008).Google Scholar
6. Shao, J., Xie, G. and Wang, L., “Leader-following formation control of multiple mobile vehicles,” IET Control Theory Appl. 1 (2), 545–552 (2007).Google Scholar
7. Defoort, M., Floquet, T., Kokosy, A. et al., “Sliding-mode formation control for cooperative autonomous mobile robots,” IEEE Trans. Ind. Electron. 55 (11), 3944–3953 (2008).CrossRefGoogle Scholar
8. Zhang, H.-T., Chen, Z., Yan, L. and Yu, W., “Applications of collective circular motion control to multirobot systems,” IEEE Trans. Control Syst. Technol. 21 (4), 1416–1422 (2013).Google Scholar
9. Dierks, T., Brenner, B. and Jagannathan, S., “Neural network-based optimal control of mobile robot formations with reduced information exchange,” IEEE Trans. Control Syst. Technol. 21 (4), 1407–1415 (2013).Google Scholar
10. Jin, Y. Sung and Park, B. S., “Formation tracking control for a class of multiple mobile robots in the presence of unknown skidding and slipping,” IET Control Theory Appl. 7 (5), 635–645 (2013).Google Scholar
11. Peng, Z., Wen, G., Rahmani, A. and Yu, Y., “Leader-follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach,” Robot. Auton. Syst. 61 (9), 988–996 (2013).Google Scholar
12. Do, K. D., “Output-feedback formation tracking control of unicycle-type mobile robots with limited sensing ranges,” Robot. Auton. Syst. 57 (1), 34–47 (2009).Google Scholar
13. Sun, T., Liu, F., Pei, H. et al., “Brief paper-observer-based adaptive leader-following formation control for non-holonomic mobile robots,” IET Control Theory Appl. 6 (18), 2835–2841 (2012).Google Scholar
14. Park, B. S., Jin-Bae, P. and Ho, C. Yoon, “Adaptive formation control of electrically driven nonholonomic mobile robots with limited information,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 41 (4), 1061–1075 (2011).Google Scholar
15. Ghommam, J., Mehrjerdi, H. and Saad, M., “Robust formation control without velocity measurement of the leader robot,” Control Eng. Pract. 21 (8), 1143–1156 (2013).Google Scholar
16. David, J. and Manivannan, P. V., “Control of truck-trailer mobile robots: A survey,” Intell. Serv. Robot. 7 (4), 245–258 (2014).Google Scholar
17. Khalaji, A. K. and Moosavian, A. A., “Robust adaptive controller for a tractor-trailer mobile robot,” IEEE/ASME Trans. Mechatron. 19 (3), 943–953 (2014).Google Scholar
18. Yuan, J., Sun, F. and Huang, Y., “Trajectory generation and tracking control for double-steering tractor-trailer mobile robots with on-axle hitching,” IEEE Trans. Ind. Electron. 62 (12), 7665–7677 (2015).Google Scholar
19. Michalek, M. M. and Kielczewski, M., “The concept of passive control assistance for docking maneuvers with N-Trailer vehicles,” IEEE/ASME Trans. Mechatron. 20 (5), 2075–2084 (2015).Google Scholar
20. Kayacan, E., Ramon, H. and Saeys, W., “Robust trajectory tracking error model-based predictive control for unmanned ground vehicles,” IEEE/ASME Trans. Mechatron. 21 (2), 806–814 (2016).Google Scholar
21. Kayacan, E., Kayacan, E., Ramon, H. and Saeys, W., “Robust tube-based decentralized nonlinear model predictive control of an autonomous tractor-trailer system,” IEEE/ASME Trans. Mechatron. 20 (1), 447–456 (2015).CrossRefGoogle Scholar
22. Kayacan, E., Kayacan, E., Ramon, H. and Saeys, W., “Learning in centralized nonlinear model predictive control: Application to an autonomous tractor-trailer system,” IEEE Trans. Control Syst. Technol. 23 (1), 197–205 (2015).Google Scholar
23. Matsushita, K. and Murakami, T., “Nonholonomic equivalent disturbance based backward motion control of tractor-trailer with virtual steering,” IEEE Trans. Ind. Electron. 55 (1), 280–287 (2008).Google Scholar
24. Astolfi, A., Bolzern, P. and Locatelli, A., “Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: A Lyapunov-based approach,” IEEE Trans. Robot. Autom. 20 (1), 154–160 (2004).Google Scholar
25. Hao, Y. and Agrawal, S. K., “Formation planning and control of UGVs with trailers,” Auton. Robot. 19, 257–270 (2005).Google Scholar
26. Lewis, F., Dawson, D. M. and Abdallah, C. T., Robot Manipulator Control Theory and Practice, 2nd Edition, Revised and Expanded (Marcel Dekker: New York, 2004).Google Scholar
27. Tee, K. P. and Ge, S. S., “Control of fully actuated ocean surface vessels using a class of feedforward approximators,” IEEE Trans. Control Syst. Technol. 14 (4), 750–756 (2006).Google Scholar
28. Ge, S. S. and Zhang, J., “Neural-network control of nonaffine nonlinear system with zeros dynamics by state and output feedback,” IEEE Trans. Neural Netw. 14 (4), 900–918 (2003).Google Scholar
29. Tee, K. P., Ge, S. S. and Tay, F. E. H., “Adaptive neural network control for helicopters in vertical flight,” IEEE Trans. Control Syst. Technol. 16 (4), 753–762 (2008).Google Scholar
30. Yao, B., Adaptive Robust Control of Nonlinear Systems with Application to Control of Mechanical Systems Ph.D. Thesis (Berkeley: University of California, 1996).Google Scholar