Shiqi Li,∗ Dong Chen,∗ and Junfeng Wang∗


  1. [1] Y.D. Patel and P.M. George, Parallel manipulators applications: A survey, Modern Mechanical Engineering, 2(3), 2012, 57–64.
  2. [2] W.W. Shang and S. Cong, Nonlinear computed torque control for a high-speed planar parallel manipulator, Mechatronics, 19(6), 2009, 987–992.
  3. [3] Z.C. Pei, Y.F. Zhang, and Z.Y. Tang, Model reference adaptive PID control of hydraulic parallel robot based on RBF neural network, 2007 IEEE International Conf. on Robotics and Biomimetics, Sanya, IEEE, Jan 2008, 1383–1387.
  4. [4] Q. Zhao, P.F. Wang, and J.P. Mei, Controller parameter tuning of delta robot based on servo identification, Chinese Journal of Mechanical Engineering, 28(2), 2015, 267–275.
  5. [5] D. Wang, J. Wu, L. Wang, Y. Liu, and G. Yu, A method for designing control parameters of a 3-DOF parallel tool head, Mechatronics, 41, 2017, 102–113.
  6. [6] J.Y. Wang, Y.G. Zhu, R.L. Qi, X.G. Zheng, and W. Li, Adaptive PID control of multi-DOF industrial robot based on neural network, Journal of Ambient Intelligence and Humanized, 6, 2020, 1–12.
  7. [7] N. Kumar, V. Panwar, N. Sukavanam, S.P. Sharma, and J.H. Borm, Neural network-based nonlinear tracking control of kinematically redundant robot manipulators, Mathematical and Computer Modeling, 53(9–10), 2011, 1889–1901.
  8. [8] S.K. Wang, J.Z. Wang, and D.W. Shi, CMAC-based compound control of hydraulically driven 6-DOF parallel manipulator, Journal of Mechanical Science and Technology, 25(6), 2011, 1595–1602.
  9. [9] H. Pham and H. Anh, Online tuning gain scheduling MIMO neural PID control of the 2-axes pneumatic artificial muscle (PAM) robot arm, Expert Systems with Applications, 37, 2011, 6547–6560.
  10. [10] Z.D. Zhou, W. Meng, Q.S. Ai, Q. Liu, and X. Wu, Practical velocity tracking control of a parallel robot based on fuzzy adaptive algorithm, Advances in Mechanical Engineering, 5, 2013, 323–335.
  11. [11] C.H. Lee and C.C. Teng, Calculation of PID controller parameters by using a fuzzy neural network, ISA Transactions, 42(3), 2003, 391–400.
  12. [12] M.Z. Alfaiz and S.A. Sadeq, Particle swarm optimization based fuzzy-neural like PID controller for TCP/AQM router, Intelligent Control and Automation, 3(1), 2012, 71–77.
  13. [13] D.C. Theodoridis, Y.S. Boutalis, and M.A. Christodoulou, A new adaptive neuro-fuzzy controller for trajectory tracking of robot manipulators, International Journal of Robotics and Automation, 26(1), 2011, 64–75.
  14. [14] C.G. Zhang and L.F. Zhang, Study on parameters optimization method of fuzzy neural network PID controller, International Journal of Control and Automation, 7(3), 2014, 45–54.
  15. [15] Y.N. Wang, Y.L. Chenxie, J.H. Tan, C. Wang, Y.Y. Wang, and Y.W. Zhang, Fuzzy radial basis function neural network PID control system for a quadrotor UAV based on particle swarm optimization, 2015 IEEE International Conf. on Information and Automation, Lijiang, IEEE, Oct 2015, 2580–2585.
  16. [16] Y.M. Jiang, C.G. Yang and H.B. Ma, A review of fuzzy logic and neural network based intelligent control design for discretetime systems, Discrete Dynamics in Nature and Society, 4, 2016, 1–11.
  17. [17] H.X. Huang, J.C. Li, and C.L. Xiao, A proposed iteration optimization approach integrating back propagation neural network with genetic algorithm, Expert Systems with Applications, 42(1), 2015, 146–155.
  18. [18] R.D. Leone, R. Capparuccia, and E. Merelli, A successive over-relaxation back propagation algorithm for neural-network training, IEEE Transactions on Neural Networks, 9(3), 1998, 381–388.
  19. [19] R. Furtuna, S. Curteanu, and M. Cazacu, Optimization methodology applied to feed-forward artificial neural network parameters, International Journal of Quantum Chemistry, 111(3), 2011, 539–553.
  20. [20] M. Neshat, G. Sepidnam, M. Sargolzaei, and A.N. Toosi, Artificial fish swarm algorithm: A survey of the state-of-theart, hybridization, combinatorial and indicative applications, Artificial Intelligence Review, 42(4), 2012, 965–997.

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