@inproceedings{93a66988caaa484f9cd8aae57288cfff,
title = "An adaptive position synchronization controller using orthogonal neural network for 3-DOF planar parallel manipulators",
abstract = "This paper proposes an adaptive position synchronization controller using orthogonal neural network for 3-DOF planar parallel manipulators. The controller is designed based on the combination of computed torque method with position synchronization technique and orthogonal neural network. By using the orthogonal neural network with online turning gains can overcome the drawbacks of the traditional feedforward neural network such as initial values of weights, number of processing elements, slow convergence speed and the difficulty of choosing learning rate. To evaluate the effectiveness of the proposed control strategy, simulations were conducted by using the combination of SimMechanics and Solidworks. The tracking control results of the parallel manipulators were significantly improved in comparison with the performance when applying non-synchronization controllers.",
keywords = "planar parallel manipulator, position synchronization controller, adaptive controller, an orthogonal neural network, online self-turning",
author = "Le, {Quang Dan} and Hee-Jun Kang",
year = "2017",
month = jul,
day = "21",
doi = "10.1007/978-3-319-63315-2_1",
language = "English",
isbn = "9783319633145",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer",
pages = "3--14",
editor = "De-Shuang Huang and Kyungsook Han and Gromiha, {M. Michael}",
booktitle = "13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017, Proceedings, Part III 13",
}