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.
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Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03930496).
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Le, Q.D., Kang, HJ., Le, T.D. (2017). An Adaptive Position Synchronization Controller Using Orthogonal Neural Network for 3-DOF Planar Parallel Manipulators. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_1
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