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Design and application of an adaptive backstepping sliding mode controller for a six-DOF quadrotor aerial robot

Published online by Cambridge University Press:  03 August 2018

Mohd Ariffanan Mohd Basri
Mohd Ariffanan Mohd Basri*
Affiliation:
Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
*
*Corresponding author. E-mail: [email protected]
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Summary

The quadrotor aerial robot is a complex system and its dynamics involve nonlinearity, uncertainty, and coupling. In this paper, an adaptive backstepping sliding mode control (ABSMC) is presented for stabilizing, tracking, and position control of a quadrotor aerial robot subjected to external disturbances. The developed control structure integrates a backstepping and a sliding mode control approach. A sliding surface is introduced in a Lyapunov function of backstepping design in order to further improve robustness of the system. To attenuate a chattering problem, a saturation function is used to replace a discontinuous sign function. Moreover, to avoid a necessity for knowledge of a bound of external disturbance, an online adaptation law is derived. Particle swarm optimization (PSO) algorithm has been adopted to find parameters of the controller. Simulations using a dynamic model of a six degrees of freedom (DOF) quadrotor aerial robot show the effectiveness of the approach in performing stabilization and position control even in the presence of external disturbances.

Keywords

Quadrotor aerial robotBackstepping controlAdaptive controlSliding mode controlParticle swarm optimization
Type
Articles
Information
Robotica , Volume 36 , Issue 11 , November 2018 , pp. 1701 - 1727
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Sun, Y., Xian, N. and Duan, H., “Linear-quadratic regulator controller design for quadrotor based on pigeon-inspired optimization,” Aircr. Eng. Aerosp. Technol. 88 (6), 761–770 (2016).Google Scholar
2. Yiqun, D., Jun, F., Bin, Y., Youmin, Z. and Liang, A. Jian, “Position and Heading Angle Control of an Unmanned Quadrotor Helicopter Using LQR Method,” Proceedings of the 34th Chinese Control Conference (CCC) Hangzhou, China (2015) pp. 5566–5571.Google Scholar
3. Rinaldi, F., Chiesa, S. and Quagliotti, F., “Linear quadratic control for quadrotors UAVs dynamics and formation flight,” J. Intell. Robot. Syst. 70 (1–4), 203–220 (2013).Google Scholar
4. Sabzevari, D., Kargar, S. M. and Zanjani, S. M. A., “Mathematical modeling and designing PID controller for a Quadrotor and optimize its step response by Genetic Algorithm,” Majlesi J. Electr. Eng. 10 (4), 17–24 (2016).Google Scholar
5. Junior, J. C. V., De Paula, J. C., Leandro, G. V. and Bonfim, M. C., “Stability control of a quad-rotor using a PID controller,” Braz. J. Instrum. Control 1 (1), 15–20 (2013).Google Scholar
6. Noordin, A., Basri, M. A. M., Mohamed, Z. and Abidin, A. F. Z., “Modelling and PSO fine-tuned PID control of quadrotor UAV,” Int. J. Adv. Sci. Eng. Inf. Technol. 7 (4), 1367–1373 (2017).Google Scholar
7. Prayitno, A., Indrawati, V. and Utomo, G., “Trajectory tracking of AR. Drone quadrotor using fuzzy logic controller,” TELKOMNIKA 12 (4), 819–828 (2014).Google Scholar
8. Torres, F., Rabhi, A., Lara, D., Romero, G. and Pégard, C., “Fuzzy state feedback for attitude stabilization of quadrotor,” Int. J. Adv. Robot. Syst. 13 (1), 2 (2016).Google Scholar
9. Zheng, E.-H., Xiong, J.-J. and Luo, J.-L., “Second order sliding mode control for a quadrotor UAV,” ISA Trans. 53 (4), 1350–1356 (2014).Google Scholar
10. Sumantri, B., Uchiyama, N., Sano, S. and Kawabata, Y., “Robust tracking control of a quad-rotor helicopter utilizing sliding mode control with a nonlinear sliding surface,” J. Syst. Des. Dyn. 7 (2), 226–241 (2013).Google Scholar
11. Xiao, B. and Yin, S., “A new disturbance attenuation control scheme for quadrotor unmanned aerial vehicles,” IEEE Trans. Ind. Inf. 13 (6), 2922–2932 (2017).Google Scholar
12. Huo, X., Huo, M. and Karimi, H. R., “Attitude stabilization control of a quadrotor UAV by using backstepping approach,” Math. Probl. Eng. 1–9 (2014).Google Scholar
13. Suicmez, E. C. and Kutay, A. T., “Path tracking control of a quadrotor UAV with backstepping method,” J. Aeronaut. Space Technol. 7 (2), 1–13 (2014).Google Scholar
14. Basri, M. A. M., Husain, A. R. and Danapalasingam, K. A., “Backstepping controller with intelligent parameters selection for stabilization of quadrotor helicopter,” J. Eng. Sci. Technol. Rev. 7 (2), 66–74 (2014).Google Scholar
15. Bartolini, G., Fridman, L., Pisano, A. and Usai, E., “Modern sliding mode control theory,” Lecture Notes Control Inf. Sci. 375 (7), 23–49 (2008).Google Scholar
16. Hallaj, M. A. A. and Assadian, N., “Sliding mode control of electromagnetic tethered satellite formation,” Adv. Space Res. 58 (4), 619–634 (2016).Google Scholar
17. Turner, B., Williams, J., Shtessel, Y. and Adhami, R., “Integral Sliding Mode Autopilot for Rocket Stabilization with Unmatched Disturbances,” Proceedings of AIAA Guidance, Navigation, and Control Conference, Minneapolis, MN, USA (2012) p. 4466.Google Scholar
18. Rao, D. V. and Sinha, N. K., “A sliding mode controller for aircraft simulated entry into spin,” Aerosp. Sci. Technol. 28 (1), 154–163 (2013).Google Scholar
19. Chen, C. T., “A sliding mode control strategy for robust temperature trajectory tracking of a batch reactor,” Chem. Eng. Commun. 201 (1), 1–22 (2014).Google Scholar
20. Zhong, C., Guo, Y., Zhou, C. and Chen, Q., “On sliding mode variable structure control for flexible spacecraft and chattering reduction,” Aerosp. Control Appl. 39 (2), 23–27 (2013).Google Scholar
21. Pukdeboon, C., “Lyapunov optimizing sliding mode control for robot manipulators,” Appl. Math. Sci. 7 (63), 3123–3139 (2013).Google Scholar
22. Maria Joseph, F. O. and Podder, T., “Sliding mode control of a shape memory alloy actuated active flexible needle,” Robotica 36 (8), 1188–1205 (2018).Google Scholar
23. Guo, Z. Q., Xu, J. X. and Lee, T. H., “Design and implementation of a new sliding mode controller on an underactuated wheeled inverted pendulum,” J. Franklin Inst. 351 (4), 2261–2282 (2014).Google Scholar
24. Kennedy, J., Eberhart, R., “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, WA, Australia (1995) pp. 1942–1948.Google Scholar
25. Abido, M., “Optimal design of power-system stabilizers using particle swarm optimization,” IEEE Trans. Energy Convers. 17 (3), 406–413 (2002).Google Scholar
26. Rini, D. P., Shamsuddin, S. M. and Yuhaniz, S. S., “Particle swarm optimization: Technique, system and challenges,” Int. J. Comput. Appl. 14 (1), 19–27 (2011).Google Scholar
27. Marinaki, M., Marinakis, Y. and Stavroulakis, G. E., “Fuzzy control optimized by a multi-objective particle swarm optimization algorithm for vibration suppression of smart structures,” Struct. Multidiscip. Optim. 43 (1), 29–42 (2011).Google Scholar
28. Zuo, Z., “Trajectory tracking control design with command-filtered compensation for a quadrotor,” IET Control Theory Appl. 4 (11), 2343–2355 (2010).Google Scholar
29. Olfati-Saber, R., Nonlinear Control of Underactuated Mechanical Systems with Application to Robotics and Aerospace Vehicles Ph.D. Thesis (Massachusetts Institute of Technology, Cambridge, MA, USA, 2001).Google Scholar
30. Bouabdallah, S., Design and Control of Quadrotors with Application to Autonomous Flying Ph.D. Thesis (École Polytechnique Fédérale De Lausanne, Lausanne, EPFL, 2007).Google Scholar
31. Hassan, R., Cohanim, B., De Weck, O. and Venter, G., “A Comparison of Particle Swarm Optimization and the Genetic Algorithm,” Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference, Austin, TX, USA (2005).Google Scholar
32. Lalitha, M. P., Reddy, V. V. and Usha, V., “Optimal DG placement for minimum real power loss in radial distribution systems using PSO,” J. Theor. Appl. Inf. Technol. 13 (2), 107–116 (2010).Google Scholar
33. Eberhart, R. C. and Shi, Y., “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization,” Proceedings of the IEEE Congress on Evolutionary Computation, La Jolla, CA, USA (2000) pp. 84–88.Google Scholar
34. Allaoua, B., Gasbaoui, B. and Mebarki, B., “Setting up PID DC motor speed control alteration parameters using particle swarm optimization strategy,” Leonardo Electron. J. Practices Technol. 14, 19–32 (2009).Google Scholar
35. Voos, H., “Nonlinear Control of a Quadrotor Micro-UAV Using Feedback-Linearization,” IEEE International Conference on Mechatronics, Malaga, Spain (2009) pp. 1–6.Google Scholar
36. Waslander, S. L. and Wang, C., “Wind Disturbance Estimation and Rejection for Quadrotor Position Control,” AIAA Infotech@Aerospace Conference and AIAA Unmanned. . . Unlimited Conference, Seattle, WA, USA (2009).Google Scholar