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Design Boundary Layer Thickness and Switching Gain in SMC Algorithm for AUV Motion Control

Published online by Cambridge University Press:  20 March 2019

Ehsan Taheri ,
Mohamad Hossein Ferdowsi  and
Mohammad Danesh
Ehsan Taheri*
Affiliation:
Control Group, Electrical Engineering Department, Malek Ashtar University of Technology, 15875-1774, Tehran, Iran
Mohamad Hossein Ferdowsi
Affiliation:
Control Group, Electrical Engineering Department, Malek Ashtar University of Technology, 15875-1774, Tehran, Iran
Mohammad Danesh
Affiliation:
Department of Mechanical Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

Designing the boundary layer thickness and switching gain in the nonlinear part of sliding mode controller (SMC) is one of the main subjects in SMC design that needs human experience, knowledge on the amplitude of disturbances, and information about the bounds of system uncertainties. In this paper, to reduce the trial-and-error effort by the designer(s) two different fitness functions in the horizontal and vertical planes are presented and a heuristic method is used for their optimization. The optimal switching gain in the proposed approach properly compensates the unmodeled dynamics, model uncertainty, and external disturbances. Chattering phenomenon in control signals and noise measurement effect are reduced by the optimal selection of boundary layer thickness. This proposed method is applied to an autonomous underwater vehicle (AUV) and evaluated through the real-time and cost-effective manner. The execution code is implemented on a single-board computer (SBC) through the xPC Target and is evaluated by the processor-in-the-loop (PIL) test. The results of the PIL test in the two different test cases indicate that the chattering phenomenon and amplitude of control signal applied to the actuators are reduced in comparison with the three conventional approaches in the AUV motion control.

Keywords

Processor-in-the-loop testSliding mode controllerAutonomous underwater vehicleHeuristic optimization algorithm
Type
Articles
Information
Robotica , Volume 37 , Issue 10 , October 2019 , pp. 1785 - 1803
Copyright
© Cambridge University Press 2019 

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References

Yu, X. and Kaynak, O., “Sliding-mode control with soft computing: A survey,” IEEE Trans. Industr. Electron. 56(9), 3275–3285 (2009), doi: 10.1109/TIE.2009.2027531.Google Scholar
Liu, Y. and Bucknall, R., “A survey of formation control and motion planning of multiple unmanned vehicles,” Robotica 36(7), 1019–1047 (2018). doi: 10.1017/S0263574718000218.CrossRefGoogle Scholar
Cui, R., Chen, L., Yang, C. and Chen, M., “Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities,” IEEE Trans. Industr. Electron. 64(8), 6785–6795 (2017), doi: 10.1109/TIE.2017.2694410.CrossRefGoogle Scholar
Cui, R., Zhang, X. and Cui, D., “Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities,” Ocean Eng. 123, 45–54 (2016), doi: 10.1016/j.oceaneng.2016.06.041.CrossRefGoogle Scholar
Cui, R., Yang, C., Li, Y. and Sharma, S., “Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning,” IEEE Trans. Syst. Man Cybern. Syst. 47(6), 1019–1029 (2017), doi: 10.1109/TSMC.2016.2645699.CrossRefGoogle Scholar
Petrich, J. and Stilwell, D. J., “Robust control for an autonomous underwater vehicle that suppresses pitch and yaw coupling,” Ocean Eng. 38(1), 197–204 (2011), doi: 10.1016/j.oceaneng.2010.10.007.CrossRefGoogle Scholar
Gibson, S. B. and Stilwell, D. J., “An H∞Loop-shaping Design Procedure for Attitude Control of an AUV,” Proceedings of the Oceans 2016 MTS/IEEE Monterey (2016) pp. 1–7, doi: 10.1109/oceans.2016.7761167.CrossRefGoogle Scholar
Spencer, D. A. and Wang, Y., “SLQR Suboptimal Human-robot Collaborative Guidance and Navigation for Autonomous Underwater Vehicles,” American Control Conference (ACC) (2015) pp. 2131–2136, doi: 10.1109/acc.2015.7171048.CrossRefGoogle Scholar
Joo, M. G. and Qu, Z., “An autonomous underwater vehicle as an underwater glider and its depth control,” Int. J. Control Autom. Syst. 13(5), 1212–1220 (2015), doi: 10.1007/s12555-014-0252-8.CrossRefGoogle Scholar
Sarhadi, P., Ranjbar Noei, A. and Khosravi, A., “Model reference adaptive PID control with antiwindup compensator for an autonomous underwater vehicle,” Robot. Auton. Syst. 83, 87–93 (2016), doi: 10.1016/j.robot.2016.05.016.CrossRefGoogle Scholar
Huang, X., Li, Y., Du, F. and Jin, S., “Horizontal path following for underactuated AUV based on dynamic circle guidance,” Robotica 35(4), 876–891 (2017), doi: 10.1017/S0263574715000867.Google Scholar
Lari, A. and Khosravi, A., “An evolutionary approach to design practical μ synthesis controllers,” Int. J. Control Autom. Syst. 11(1), 167–174 (2013), doi: 10.1007/s12555-012-0181-3.CrossRefGoogle Scholar
Mahapatra, S. and Subudhi, B., “Design and experimental realization of a backstepping nonlinear H∞control for an autonomous underwater vehicle using a nonlinear matrix inequality approach,” Trans. Inst. Measur. Control. 40(11), 3390–3403 (2017). doi: 10.1177/0142331217721315.CrossRefGoogle Scholar
Rezazadegan, F., Shojaei, K., Sheikholeslam, F. and Chatraei, A., “A novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties,” Ocean Eng. 107, 246–258 (2015), doi: 10.1016/j.oceaneng.2015.07.040.CrossRefGoogle Scholar
Utkin, V., “Variable structure systems with sliding modes,” IEEE Trans. Autom. Control. 22(2), 212–222 (1977), doi: 10.1109/tac.1977.1101446.CrossRefGoogle Scholar
Yoerger, D. and Slotine, J., “Robust trajectory control of underwater vehicles,” Oceanic Eng. 10(4), 462–470 (1985), doi: 10.1109/joe.1985.1145131.CrossRefGoogle Scholar
Vuilmet, C., “High Order Sliding Mode Control Applied to a Heavyweight Torpedo,” Proceedings of 2005 IEEE Conference on Control Applications, 61–66 (2005), doi: 10.1109/cca.2005.1507101.CrossRefGoogle Scholar
Ghiglino, P., Forshaw, J. L. and Lappas, V. J., “Online evolutionary swarm algorithm for self-tuning unmanned flight control laws,” J. Guid. Control Dynam. 38(4), 772–782 (2015), doi: 10.2514/1.g000376.CrossRefGoogle Scholar
Poksawat, P., Wang, L. and Mohamed, A., “Automatic tuning of attitude control system for fixed-wing unmanned aerial vehicles,” IET Control Theory Appl. 10(17), 2233–2242 (2016), doi: 10.1049/iet-cta.2016.0236.CrossRefGoogle Scholar
Khoshnam, S., “Three-dimensional tracking control of autonomous underwater vehicles with limited torque and without velocity sensors,” Robotica, 36(3), 374–394 (2017), doi: 10.1017/S0263574717000455.Google Scholar
Poksawat, P., Wang, L. and Mohamed, A., “Gain scheduled attitude control of fixed-wing UAV with automatic controller tuning,” IEEE Trans. Control Syst. Technol. 99, 1–12 (2017), doi: 10.1109/tcst.2017.2709274.Google Scholar
Fernandez, D. C. and Hollinger, G. A., “Model predictive control for underwater robots in ocean waves,” IEEE Robot. Autom. Lett. 2(1), 88–95 (2017), doi: 10.1109/lra.2016.2531792.CrossRefGoogle Scholar
Aghababa, M. P., “3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles,” Appl. Ocean Res. 38, 48–62 (2012), doi: 10.1016/j.apor.2012.06.002.CrossRefGoogle Scholar
Zadeh, S. M., Yazdani, A. M., Sammut, K. and Powers, D. M. W., “Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms,” Appl. Soft Comput. 70, 929–945 (2018). doi: 10.1016/j.asoc.2017.10.025.Google Scholar
Bing, S., Daqi, Z. and Simon, Y., “Real-time hybrid design of tracking control and obstacles avoidance for underactuated underwater vehicles,” J. Intell. Fuzzy Syst. 30(5), 2541–2553 (2016), doi: 10.3233/ifs-151799.Google Scholar
Fossen, T. I., Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles (Marine Cybernetics AS, Trondheim, Norway, 2002).Google Scholar
Taheri, E., Kinodynamic Path Planning for Autonomous Underwater Vehicle Ph.D Thesis (Electrical Engineering Malek-Ashtar University of Technology, 2018).Google Scholar
Makrini, I. E., Guerrero, C. R. and Lefeber, D., “The variable boundary layer sliding mode control: A safe and performant control for compliant joint manipulators,” IEEE Robot. Autom. Lett. 2(1), 187–192 (2017), doi: 10.1109/lra.2016.2587059.Google Scholar
Zhang, X. and Guo, F., “Sliding mode-like fuzzy logic control with boundary layer self-tuning for discrete nonlinear systems,” Found. Appl. Intell. System. 213, 479–490 (2013), doi: 10.1007/978-3-642-37829-4_41.CrossRefGoogle Scholar
Seok, H., Yongho, K., Hyun, L. C., Lee, H. and Park, M., “Design of sliding-mode control based on fuzzy disturbance observer for minimization of switching gain and chattering,” Soft Comput. 19(4), 851–858 (2015), doi: 10.1007/s00500-014-1412-8.Google Scholar
Yu, C., Xiang, X., Wilson, P. and Zhang, Q.. “Guidance-error-based robust fuzzy adaptive control for bottom following of a flight-style AUV with delayed and saturated control surfaces,” IEEE Trans. Cybern. (2018).Google Scholar