ROBUST ADAPTIVE CONTROL BASED ON MACHINE LEARNING AND NTSMC FOR WORKPIECE SURFACE-GRINDING ROBOT, 444-453.

Lin Jia, Yaonan Wang, Jing He, Li Liu, Zhen Li, and Yongpeng Shen

References

  1. [1] X.U. Xiaohu, Z.H.U. Dahu, H. Zhang, et al., Application of novel force control strategies to enhance robotic abrasive belt grinding quality of aero-engine blades, Chinese Journal of Aeronautics, 32(10), 2019, 2368–2382.
  2. [2] L. Gracia, J.E. Solanes, P. Mu˜noz-Benavent, et al., Adaptive sliding mode control for robotic surface treatment using force feedback, Mechatronics, 52, 2018, 102–118.
  3. [3] J. Li, J. Wang, S.X. Yang, and S. Jia, SLAM based on information fusion of stereo vision and electronic compass, International Journal of Robotics and Automation, 69(3), 2016, 243–250.
  4. [4] L. Jia, Y. Wang, C. Zhang, et al., A robust adaptive trajectory tracking algorithm using SMC and machine learning for free-form surface grinding robots with actuator dead zones, Applied Sciences, 9(18), 2019, 3837.
  5. [5] H. Tian, L. Ma, X. Zhu, et al., Grinding method, trajectory planning and simulation of a 3 DOF knee grinding robot, International Journal of Simulation Modelling, 18(1), 2019, 150–162.
  6. [6] A. Zhu and S.X. Yang, Tracking control of a mobile robot with stability analysis, International Journal of Robotics and Automation, 28(4), 2013, 340–348.
  7. [7] S. Tan, S.X. Yang, and A. Zhu, A novel GA-based fuzzy controller for mobile robots in dynamic environments with moving obstacles, International Journal of Robotics and Automation, 26(2), 2011, 212–228.
  8. [8] M. Yan, B. Huang, D. Zhu, and S.X. Yang, A novel segmentation method based on grayscale wave for underwater images, International Journal of Robotics and Automation, 33(4), 2018, 386–393.
  9. [9] Z. Yu, Y. Shi, J. Xie, S.X. Yang, and Z. Dai, Design and analysis of a bionic adhesive foot for gecko robot climbing the ceiling, International Journal of Robotics and Automation, 33(3), 2018, 445–454.
  10. [10] Y. Wang, Y. Tuo, S.X. Yang, and M. Fu, Nonlinear model predictive control of dynamic positioning of deep-sea ships with a unified model, International Journal of Robotics and Automation, 31(6), 2016, 519–529.
  11. [11] J. Ni, X. Li, M. Hua, and S.X. Yang, Bioinspired neural network-based Q-Learning approach for robot path planning in unknown environments, International Journal of Robotics and Automation, 31(6), 2016, 464–474.
  12. [12] S. Mao, H. Wu, M. Lu, C.-W. Cheng, Multiple 3D marker localization and tracking system in image-guided radiotherapy, International Journal of Robotics and Automation, 32(5), 2017, 517–523.
  13. [13] A. Zhu and S.X. Yang, An improved approach to dynamic task assignment of non-holonomic multi-robots, International Journal of Robotics and Automation, 26(4), 2011, 362–368.
  14. [14] K. Lochan, B.K. Roy, and B. Subudhi, Robust tip trajectory synchronisation between assumed modes modelled two-link flexible manipulators using second-order PID terminal SMC, Robotics and Autonomous Systems, 97, 2017, 108–124.
  15. [15] J. Zhao, J. Zhou, S.X. Yang, and W. Zhang, A dynamic velocity regulation approach to planar trajectory tracking control of underactuated AUVs, International Journal of Robotics and Automation, 32(5), 2017, 500–508.
  16. [16] F. Luan, J. Na, Y. Huang, et al., Adaptive neural network control for robotic manipulators with guaranteed finite-time convergence, Neurocomputing, 337, 2019, 153–164.
  17. [17] Z. Yan, B. Hao, W. Zhang, and S.X. Yang, Dubins-RRT path planning and heading-vector control guidance for a UUV recovery, International Journal of Robotics and Automation,, 69(3), 2016, 251–262.
  18. [18] J. Faigl and P. ˇC´ıˇzek, Adaptive locomotion control of hexapod walking robot for traversing rough terrains with position feedback only, Robotics and Autonomous Systems, 116, 2019, 136–147.
  19. [19] P.X. Liu, M.Q.-H. Meng, J.J. Gu, and S.X. Yang, A study of Internet delays for robot teleoperation using biologically inspired approaches, International Journal of Robotics and Automation, 17(4), 2002, 186–195.
  20. [20] Z. Chu, D. Zhu, and S.X. Yang, Adaptive terminal sliding mode based sensorless speed control for underwater thruster, International Journal of Robotics and Automation, 31(3), 2016, 187–197.
  21. [21] T.L. Costa, F.A. Lara-Molina, A.A.C. Junior, et al., Robust H∞ computed torque control for manipulators, IEEE Latin America Transactions, 16(2), 2018, 398–407.
  22. [22] J. Arcos-Legarda, J. Cortes-Romero, and A. Tovar, Robust compound control of dynamic bipedal robots, Mechatronics, 59, 2019, 154–167.
  23. [23] Y. Zhou, H. Hu, Y. Liu, et al., A distributed approach to robust control of multi-robot systems, Automatica, 98, 2018, 1–13.
  24. [24] A. Zhu and S.X. Yang, A SOM-based multi-agent architecture for multi-robot systems, International Journal of Robotics and Automation, 21(2), 2006, 91–99.
  25. [25] J. Ni, X. Yang, J. Chen, and S.X. Yang, Dynamic bioinspired neural network for multi-robot formation control in unknown environments, International Journal of Robotics and Automation, 30(3), 2015, 256–266.
  26. [26] S. Refoufi and K. Benmahammed, Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms, ISA Transactions, 77, 2018, 133–145.
  27. [27] M.S. Qureshi, P. Swarnkar, and S. Gupta, A supervisory online tuned fuzzy logic based sliding mode control for robotics: an application to surgical robots Robotics and Autonomous Systems, 109, 2018, 68–85.
  28. [28] D. Zhu, J. Liu, and S.X. Yang, Particle swarm optimization approach to thrusters fault-tolerant control of unmanned underwater vehicles, International Journal of Robotics and Automation, 26(3), 2011, 282–287.
  29. [29] B. Sun, D. Zhu, and S.X. Yang, A bio-inspired cascaded approach for three-dimensional tracking control of unmanned underwater vehicles, International Journal of Robotics and Automation, 29(4), 2014, 349–358.
  30. [30] J. Zhang, F. Tian, S.X. Yang, Y. Liu, Z. Liang, and D. Wang, An intelligent and automatic control method for tobacco flue curing based on machine learning, International Journal of Robotics and Automation, 31(6), 2016, 509–518.
  31. [31] Z. Chen, X. Yang, and X. Liu, RBFNN-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics, Neurocomputing, 362, 2019, 72–82.
  32. [32] F. Wang, Z. Chao, L. Huang, et al., Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode, Cluster Computing, 22(3), 2019, 5799–5809.
  33. [33] J. He, L. Mi, J. Liu, X. Cheng, Z. Lin, and C. Zhang, Ring coupling-based collaborative fault-tolerant control for multi-robot actuator fault, International Journal of Robotics and Automation, 33(6), 2018, 672–680.
  34. [34] S. Yi and J. Zhai, Adaptive second-order fast nonsingular terminal sliding mode control for robotic manipulators, ISA Transactions, 90, 2019, 41–51.
  35. [35] G. Chen, B. Jin, and Y. Chen, Nonsingular fast terminal sliding mode posture control for six-legged walking robots with redundant actuation, Mechatronics, 50, 2018, 1–15.
  36. [36] D.X. Ba, H. Yeom, and J. Bae, A direct robust nonsingular terminal sliding mode controller based on an adaptive time-delay estimator for servomotor rigid robots, Mechatronics, 59, 2019, 82–94.
  37. [37] H. Wang, Adaptive control of robot manipulators with uncertain kinematics and dynamics, IEEE Transactions on Automatic Control, 62(2), 2017, 948–954.
  38. [38] W. He, Y. Dong, and C. Sun, Adaptive neural network control of unknown non-linear affine systems with input dead zone and output constraint, ISA Transactions, 58, 2015, 96–104. 452
  39. [39] M. Van, S.S. Ge, and H. Ren, Finite time fault tolerant control for robot manipulators using time delay estimation and continuous nonsingular fast terminal sliding mode control, IEEE Transactions on Cybernetics, 47(7), 2017, 1681–1693.
  40. [40] J.K. Liu, Robot control system design and MATLAB simulation: the advanced design method (Beijing: Tsinghua University Press, 2017).
  41. [41] S.S. Ge, T.H. Lee, and C.J. Harri, Adaptive neural network control of robotic manipulators (London: World Scientific, 1998).

Important Links:

Go Back