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Adaptive Neural Network Control for Constrained Robot Manipulators

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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This paper presents an adaptive neural network (NN) control strategy for robot manipulators with uncertainties and constraints. Position, velocity and control input constraints are considered and tackled by introducing barrier Lyapunov functions in the backstepping procedure. The system uncertainties are estimated and compensated by a locally weighted online NN. The boundedness of the closed-loop control system and the feasibility of the proposed control law are demonstrated by theoretical analysis. The effectiveness of the proposed control strategy has been verified by simulation results on a robot manipulator.

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  1. Huh, S.H., Bien, Z.: Robust sliding mode control of a robot manipulator based on variable structure-model reference adaptive control approach. IET Control Theory Appl. 1(5), 1355–1363 (2007)

    Article  MathSciNet  Google Scholar 

  2. Islam, S., Liu, X.P.: Robust sliding mode control for robot manipulators. IEEE Trans. Ind. Electron. 58(6), 2444–2453 (2011)

    Article  Google Scholar 

  3. Sun, T., Pei, H., Pan, Y., Zhou, H., Zhang, C.: Neural network-based sliding mode adaptive control for robot manipulators. Neurocomputing 74(14), 2377–2384 (2011)

    Article  Google Scholar 

  4. Sun, T., Pei, H., Pan, Y., Zhang, C.: Robust adaptive neural network control for environmental boundary tracking by mobile robots. Int. J. Robust Nonlinear Control 23(2), 123–136 (2013)

    Article  MATH  Google Scholar 

  5. Chen, L., Hou, Z.G., Tan, M.: Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica 45(10), 2312–2318 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Li, T., Duan, S., Liu, J., Wang, L., Huang, T.: A spintronic memristor-based neural network with radial basis function for robotic manipulator control implementation. IEEE Trans. Syst. Man Cybern.: Syst. 46(4), 582–588 (2016)

    Article  Google Scholar 

  7. Pan, Y., Liu, Y., Xu, B., Yu, H.: Hybrid feedback feedforward: an efficient design of adaptive neural network control. Neural Netw. 76, 122–134 (2016)

    Article  Google Scholar 

  8. Patino, H.D., Carelli, R., Kuchen, B.R.: Neural networks for advanced control of robot manipulators. IEEE Trans. Neural Netw. 13(2), 343–354 (2002)

    Article  Google Scholar 

  9. Wai, R.J., Muthusamy, R.: Design of fuzzy-neural-network-inherited backstepping control for robot manipulator including actuator dynamics. IEEE Trans. Fuzzy Syst. 22(4), 709–722 (2014)

    Article  Google Scholar 

  10. Wai, R.J., Chen, P.C.: Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network. IEEE Trans. Fuzzy Syst. 12(4), 552–560 (2004)

    Article  MATH  Google Scholar 

  11. Seo, D.: Adaptive control for robot manipulator with guaranteed transient performance. In: IEEE Conference on Decision and Control, pp. 2109–2114 (2016)

    Google Scholar 

  12. Ngo, K.B., Mahony, R.: Bounded torque control for robot manipulators subject to joint velocity constraints. In: IEEE International Conference on Robotics and Automation, pp. 7–12 (2006)

    Google Scholar 

  13. Papageorgiou, X., Kyriakopoulos, K.J.: Motion tasks for robot manipulators subject to joint velocity constraints. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2139–2144 (2008)

    Google Scholar 

  14. Zhang, Z., Zhang, Y.: Variable joint-velocity limits of redundant robot manipulators handled by quadratic programming. IEEE/ASME Trans. Mechatron. 18(2), 674–686 (2013)

    Article  Google Scholar 

  15. Zhang, Y., Ge, S.S., Lee, T.H.: A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators. IEEE Trans. Syst. Man Cybern. B Cybern. 34(5), 2126–2132 (2004)

    Article  Google Scholar 

  16. Subudhi, B., Pradhan, S.K.: Direct adaptive control of a flexible robot using reinforcement learning. In: 2010 International Conference on Industrial Electronics, Control & Robotics, pp. 27–29 (2010)

    Google Scholar 

  17. He, W., Chen, Y., Yin, Z.: Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans. Cybern. 46, 620–629 (2016)

    Article  Google Scholar 

  18. He, W., David, A.O., Yin, Z., Sun, C.: Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans. Syst. Man Cybern. Part A-Syst. 46(6), 759–770 (2016)

    Article  Google Scholar 

  19. Kwan, C., Lewis, F.L.: Robust backstepping control of nonlinear systems using neural networks. IEEE Trans. Syst. Man Cybern. Part A-Syst. 30(6), 753–766 (2000)

    Article  Google Scholar 

  20. Kuljaca, O., Swamy, N., Lewis, F.L., Kwan, C.: Design and implementation of industrial neural network controller using backstepping. IEEE Trans. Ind. Electron. 50(1), 193–201 (2003)

    Article  Google Scholar 

  21. Liu, Y.J., Li, J., Tong, S.C., Philip Chen, C.L.: Neural nework control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE Trans. Neural Netw. Learn. Syst. 27(7), 1562–1570 (2016)

    Article  MathSciNet  Google Scholar 

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This work was supported by the National Natural Science Foundation of China under Grant No. 61503158, the MoE Tier 1 Grant from the Ministry of Education, Singapore, under WBS R-397-000-218-112, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Correspondence to Haoyong Yu .

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Wang, G., Sun, T., Pan, Y., Yu, H. (2017). Adaptive Neural Network Control for Constrained Robot Manipulators. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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