Abstract
This paper proposes a finite-time adaptive backstepping control for an n-link flexible joint manipulator based on neural network approximation. In each recursive step, an adaptive virtual controller or practical controller is designed to guarantee that all the state errors can converge into a small region within a finite time. Besides, two simple neural networks are employed to approximate and compensate for the lumped uncertainties, and the finite time stability analysis is provided based on Lyapunov synthesis. Finally, simulation results show the effectiveness of the proposed method.
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References
Li, Z., Yang, C., Tang, Y.: Decentralised adaptive fuzzy control of coordinated multiple mobile manipulators interacting with non-rigid environments. IET Control Theory Appl. 7(3), 397–410 (2013)
He, W., David, A.O., Yin, Z., et al.: Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans. Syst. Man Cybern. Syst. 46(6), 759–770 (2016)
He, W., Amoateng, D.O., Yang, C., et al.: Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis. IET Control Theory Appl. 11(4), 567–575 (2017)
Na, J., Chen, Q., Ren, X., et al.: Adaptive prescribed performance motion control of servo mechanisms with friction compensation. IEEE Trans. Industr. Electron. 61(1), 486–494 (2013)
Liu, H., Tian, X., Wang, G., et al.: Finite-Time \({H_\infty }\) control for high-precision tracking in robotic manipulators using backstepping control. IEEE Trans. Industr. Electron. 63(9), 5501–5513 (2016)
Wang, M., Yang, A.: Dynamic learning from adaptive neural control of robot manipulators with prescribed performance. IEEE Trans. Syst. Man Cybern. Syst. PP(99), 1–12 (2017)
Dai, S.L., Wang, M., et al.: Learning from adaptive neural output feedback control of robot manipulators. IFAC Proc. Vols. 46(20), 737–742 (2013)
Wang, L., Chai, T., Zhai, L.: Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics. IEEE Trans. Industr. Electron. 56(9), 3296–3304 (2009)
Hu, Q., Xu, L., Zhang, A.: Adaptive backstepping trajectory tracking control of robot manipulator. J. Franklin Inst. 349(3), 1087–1105 (2012)
Ghorbel, F., Hung, J.Y., Spong, M.W.: Adaptive control of flexible joint manipulators. IEEE J. Mag. 9(7), 9–13 (1989)
Yoo, S.J.: Distributed adaptive containment control of networked flexible-joint robots using neural networks. Expert Syst. Appl. 41(2), 470–477 (2014)
Li, Y., Tong, S., Li, T.: Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping. Nonl. Anal. Real World Appl. 14(1), 483–494 (2013)
Yoo, S.J., Park, J.B., Choi, Y.H.: Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach. IEEE Trans. Neural Networks 19(10), 1712–1726 (2008)
Haimo, V.T.: Finite time controllers. Soc. Industr. Appl. Math. 24(4), 760–770 (1986)
Zheng, J.F., Feng, Y., Zheng, X.M., et al.: Adaptive backstepping-based terminal-sliding-mode control for uncertain nonlinear systems. Control Theory Appl. Chin. 26(4), 410–414 (2009)
Zheng, X., Li, L., Zheng, J., et al.: Non-singular terminal sliding mode backstepping control for the uncertain chaotic systems. In: 2nd International Symposium on Systems and Control in Aerospace and Astronautics, pp. 1–5. IEEE Press, Shenzhen (2008)
Tang, X., Chen, Q., Nan, Y., et al.: Backstepping funnel control for prescribed performance of robotic manipulators with unknown dead zone. In: The 27th Chinese Control and Decision Conference, pp. 1508–1513. IEEE Press, Qingdao (2015)
Lu, K., Xia, Y., Yu, C., et al.: Finite-time tracking control of rigid spacecraft under actuator saturations and faults. IEEE Trans. Autom. Sci. Eng. 13(1), 368–381 (2016)
Yu, S., Yu, X., Shirinzadeh, B., Man, Z.: Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica 41, 1957–1964 (2005)
Modares, H., Ranatunga, I., Lewis, F.L., et al.: Optimized assistive human-robot interaction using reinforcement learning. IEEE Trans. Cybern. 46(3), 655–667 (2016)
Acknowledgments
The authors would thank the support from the National Natural Science Foundation (NNSF) of China under Grant No. 61573320, No. 61473262 and No. 61403343, and Zhejiang Provincial Natural Science Foundation under Grant No. Y17F030063.
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Chen, Q., Shi, H., Sun, M. (2017). Neural Network Based Finite-Time Adaptive Backstepping Control of Flexible Joint Manipulators. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_43
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DOI: https://doi.org/10.1007/978-3-319-70136-3_43
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