Shiqi Li,∗ Dong Chen,∗ and Junfeng Wang∗
[1] Y.D. Patel and P.M. George, Parallel manipulators applications: A survey, Modern Mechanical Engineering, 2(3), 2012, 57–64. [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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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.
Important Links:
Go Back