Gained switching-based fuzzy sliding mode control for a discrete-time underactuated robotic system with uncertainties

Hui LI, Ruiqin LI, Jianwei ZHANG

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PDF(1028 KB)
Front. Mech. Eng. ›› 2021, Vol. 16 ›› Issue (2) : 353-362. DOI: 10.1007/s11465-020-0620-4
RESEARCH ARTICLE
RESEARCH ARTICLE

Gained switching-based fuzzy sliding mode control for a discrete-time underactuated robotic system with uncertainties

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Abstract

This study proposes a gained switching-based discrete-time sliding mode control method to address the chattering issue in disturbed discrete-time systems, which suffer from various unknown uncertainties. Through the new structure of the designed reaching law, the proposed method can effectively increase the convergence speed while guaranteeing chattering-free control. The performance of controlling underactuated robotic systems can be further improved by the adoption of fuzzy logic to perform adaptive online hyper-parameter tuning. In addition, an underactuated robotic system with uncertainties is studied to validate the effectiveness of the proposed reaching law. Results reveal the dynamic performance and robustness of the proposed reaching law in the studied system and prove the proposed method’s superiority over other state-of-the-art methods.

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Keywords

sliding-mode control / robot control / discrete-time uncertain systems / fuzzy logic

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Hui LI, Ruiqin LI, Jianwei ZHANG. Gained switching-based fuzzy sliding mode control for a discrete-time underactuated robotic system with uncertainties. Front. Mech. Eng., 2021, 16(2): 353‒362 https://doi.org/10.1007/s11465-020-0620-4

References

[1]
Pounds P, Mahony R, Corke P. Modelling and control of a quad-rotor robot. In: Proceedings of 2006 Australasian Conference on Robotics and Automation. Auckland: Australian Robotics and Automation Association, 2006
[2]
Weng C Y, Yuan Q, Suarez-Ruiz F, A telemanipulation-based human-robot collaboration method to teach aerospace masking skills. IEEE Transactions on Industrial Informatics, 2020, 16(5): 3076–3084
CrossRef Google scholar
[3]
Schulte H, Hahn H. Fuzzy state feedback gain scheduling control of servo-pneumatic actuators. Control Engineering Practice, 2004, 12(5): 639–650
CrossRef Google scholar
[4]
Anh H P H. Online tuning gain scheduling MIMO neural PID control of the 2-axes pneumatic artificial muscle (PAM) robot arm. Expert Systems with Applications, 2010, 37(9): 6547–6560
CrossRef Google scholar
[5]
Chiu C S, Lian K Y, Liu P. Fuzzy gain scheduling for parallel parking a car-like robot. IEEE Transactions on Control Systems Technology, 2005, 13(6): 1084–1092
CrossRef Google scholar
[6]
Sardellitti I, Medrano-Cerda G A, Tsagarakis N, Gain scheduling control for a class of variable stiffness actuators based on lever mechanisms. IEEE Transactions on Robotics, 2013, 29(3): 791–798
CrossRef Google scholar
[7]
d’Andréa-Novel B, Campion G, Bastin G. Control of nonholonomic wheeled mobile robots by state feedback linearization. The International Journal of Robotics Research, 1995, 14(6): 543–559
CrossRef Google scholar
[8]
Chwa D. Tracking control of differential-drive wheeled mobile robots using a backstepping-like feedback linearization. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 2010, 40(6): 1285–1295
CrossRef Google scholar
[9]
Liu L, Liu Y J, Chen A, Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems. Science China. Information Sciences, 2020, 63(3): 132203
CrossRef Google scholar
[10]
Pan Y, Wang H, Li X, Adaptive command-filtered backstep-ping control of robot arms with compliant actuators. IEEE Transactions on Control Systems Technology, 2018, 26(3): 1149–1156
CrossRef Google scholar
[11]
Chen C P, Liu Y J, Wen G X. Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems. IEEE Transactions on Cybernetics, 2014, 44(5): 583–593
CrossRef Google scholar
[12]
Liu Y J, Lu S, Tong S. Neural network controller design for an uncertain robot with time-varying output constraint. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 2017, 47(8): 2060–2068
CrossRef Google scholar
[13]
Wang H, Pan Y, Li S, Robust sliding mode control for robots driven by compliant actuators. IEEE Transactions on Control Systems Technology, 2019, 27(3): 1259–1266
CrossRef Google scholar
[14]
Wang H, Zhang Q, Xian J, Robust finite-time output feed-back control for systems with unpredictable time-varying disturbances. IEEE Access: Practical Innovations, Open Solutions, 2020, 8: 52268–52277
CrossRef Google scholar
[15]
Wang Y, Jiang B, Wu Z, Adaptive sliding mode fault-tolerant fuzzy tracking control with application to unmanned marine vehicles. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 2020, 1–10
CrossRef Google scholar
[16]
Wang Y, Xia Y, Li H, A new integral sliding mode design method for nonlinear stochastic systems. Automatica, 2018, 90: 304–309
CrossRef Google scholar
[17]
Yang J, Li S, Yu X. Sliding-mode control for systems with mismatched uncertainties via a disturbance observer. IEEE Transactions on Industrial Electronics, 2013, 60(1): 160–169
CrossRef Google scholar
[18]
Feng Y, Yu X, Man Z. Non-singular terminal sliding mode control of rigid manipulators. Automatica, 2002, 38(12): 2159–2167
CrossRef Google scholar
[19]
Yu S, Yu X, Shirinzadeh B, Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica, 2005, 41(11): 1957–1964
CrossRef Google scholar
[20]
Chen M S, Chen C H, Yang F Y. An LTR-observer-based dynamic sliding mode control for chattering reduction. Automatica, 2007, 43(6): 1111–1116
CrossRef Google scholar
[21]
Du H, Yu X, Chen M Z, Chattering-free discrete-time sliding mode control. Automatica, 2016, 68: 87–91
CrossRef Google scholar
[22]
Parra-Vega V, Hirzinger G. Chattering-free sliding mode control for a class of nonlinear mechanical systems. International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal, 2001, 11(12): 1161–1178
CrossRef Google scholar
[23]
Gao W, Wang Y, Homaifa A. Discrete-time variable structure control systems. IEEE Transactions on Industrial Electronics, 1995, 42(2): 117–122
CrossRef Google scholar
[24]
Sun J. Chattering-free sliding-mode variable structure control of delta operator system. Journal of Computational and Theoretical Nanoscience, 2014, 11(10): 2150–2156
CrossRef Google scholar
[25]
Qu S, Xia X, Zhang J. Dynamics of discrete-time sliding-mode-control uncertain systems with a disturbance compensator. IEEE Transactions on Industrial Electronics, 2014, 61(7): 3502–3510
CrossRef Google scholar
[26]
Veselic B, Perunicic-Drazenovic B, Milosavljevic C. High-performance position control of induction motor using discrete-time sliding-mode control. IEEE Transactions on Industrial Electronics, 2008, 55(11): 3809–3817
CrossRef Google scholar
[27]
Wang J, Gao Z, Fu Y. Chattering-free discrete-time sliding mode control with event-trigger strategy. In: Proceedings of 2018 IEEE 16th International Conference on Industrial Informatics (INDIN). New York: IEEE, 2018, 629–634
CrossRef Google scholar
[28]
Song L, Wen H, Yao Q. Variable rate reaching law for discrete time variable structure control systems. Journal of the Naval Academy Of Engineering, 1999, 3: 16–21
[29]
Yan T, Wu B, He B, A novel fuzzy sliding-mode control for discrete-time uncertain system. Mathematical Problems in Engineering, 2016, 2016: 1–9
CrossRef Google scholar
[30]
Sarpturk S, Istefanopulos Y, Kaynak O. On the stability of discrete-time sliding mode control systems. IEEE Transactions on Automatic Control, 1987, 32(10): 930–932
CrossRef Google scholar
[31]
Nagendra S, Podila N, Ugarakhod R, Comparison of reinforcement learning algorithms applied to the cart-pole problem. In: Proceedings of 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Udupi: IEEE, 2017, 26–32
CrossRef Google scholar

Acknowledgements

This research was funded by the Key Research and Development Program of Shanxi Province of China (Grant Nos. 201803D421027 and 201903D421051).

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2021 Higher Education Press
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