Application of Wavelet Neural Network in Forward Kinematics Solution of 6-RSU Co-axial Parallel Mechanism Based on Final Prediction Error

Author

Faculty of Mechanical Engineering, Urmia University of Technology, Urmia, Iran

Abstract

Application of artificial neural network (ANN) in forward kinematic solution (FKS) of a novel co-axial parallel mechanism with six degrees of freedom (6-DOF) is addressed in Current work. The mechanism is known as six revolute-spherical-universal (RSU) and constructed by 6-RSU co-axial kinematic chains in parallel form. First, applying geometrical analysis and vectorial principles the kinematic model is extracted and inverse kinematics solution is done. Due to highly nonlinear characteristic of the model, forward kinematic solution for 6-RSU is so complicated. Therefore, ANN based on wavelet analysis, as a powerful solution, is designed and applied to solve FK problem. The minimum prediction risk principle with using final prediction error (FPE) is applied to find the best and optimum topology of our proposed neural network (WNN) in this paper. Furthermore, proposed wavelet WNN is developed to approximate the specific reference trajectories for manipulated platform of mechanism and the results are obtained. Comparing the extracted results by WNN with closed form solution (CFS) demonstrates the accuracy and efficiency of the proposed WNN.

Keywords


1.     Isaksson, M., Gosselin, C. and Marlow, K., "Singularity analysis of a class of kinematically redundant parallel schönflies motion generators", Mechanism and Machine Theory,  Vol. 112, (2017), 172-191.

2.     Li, T., Li, F., Jiang, Y., Zhang, J. and Wang, H., "Kinematic calibration of a 3-p (pa) s parallel-type spindle head considering the thermal error", Mechatronics,  Vol. 43, (2017), 86-98.

3.     Mohan, S., "Error analysis and control scheme for the error correction in trajectory-tracking of a planar 2prp-ppr parallel manipulator", Mechatronics,  Vol. 46, (2017), 70-83.

4.     Pedrammehr, S., Danaei, B., Abdi, H., Masouleh, M.T. and Nahavandi, S., "Dynamic analysis of hexarot: Axis-symmetric parallel manipulator", Robotica,  Vol. 36, No. 2, (2017), 225-240.

5.     Schreiber, L.-T. and Gosselin, C., "Kinematically redundant planar parallel mechanisms: Kinematics, workspace and trajectory planning", Mechanism and Machine Theory,  Vol. 119, (2018), 91-105.

6.     Herrero, S., Pinto, C., Altuzarra, O. and Diez, M., "Analysis of the 2pru-1prs 3dof parallel manipulator: Kinematics, singularities and dynamics", Robotics and Computer-Integrated Manufacturing,  Vol. 51, (2018), 63-72.

7.     Ghasemi, J., "Kinematic synthesis of parallel manipulator via neural network approach", International Journal of Engineering Transactions C: Aspects,  Vol. 30, No. 9, (2017), 319-1325.

8.     Xu, Q., Yang, Y., Jing, Z. and Hu, S., "Forward kinematics analysis for a class of asymmetrical parallel manipulators", International Journal of Advanced Robotic Systems,  Vol. 14, No. 1, (2017), 1729881416678132.

9.     Huang, G., Guo, S., Zhang, D., Qu, H. and Tang, H., "Kinematic analysis and multi-objective optimization of a new reconfigurable parallel mechanism with high stiffness", Robotica,  Vol. 36, No. 2, (2017), 187-203.

10.   Gao, L. and Wu, W., "Forward kinematics modeling of spatial parallel linkage mechanisms based on constraint equations and the numerical solving method", Robotica,  Vol. 35, No. 2, (2017), 293-309.

11.   Sadjadian, H., Taghirad, H. and Fatehi, A., "Neural networks approaches for computing the forward kinematics of a redundant parallel manipulator", International Journal of Computational Intelligence,  Vol. 2, No. 1, (2005), 40-47.

12.   Rahmani, A. and Ghanbari, A., "Application of neural network training in forward kinematics simulation for a novel modular hybrid manipulator with experimental validation", Intelligent Service Robotics,  Vol. 9, No. 1, (2016), 79-91.

13.   Lu, Y., Wang, P., Zhao, S., Hu, B., Han, J. and Sui, C., "Kinematics and statics analysis of a novel 5-dof parallel manipulator with two composite rotational/linear active legs", Robotics and Computer-Integrated Manufacturing,  Vol. 30, No. 1, (2014), 25-33.

14.   Qazani, M.R.C., Pedrammehr, S., Rahmani, A., Danaei, B., Ettefagh, M.M., Rajab, A.K.S. and Abdi, H., "Kinematic analysis and workspace determination of hexarot-a novel 6-dof parallel manipulator with a rotation-symmetric arm system", Robotica,  Vol. 33, No. 08, (2015), 1686-1703.

15.   Qazani, M.R.C., Pedrammehr, S., Rahmani, A., Shahryari, M., Rajab, A.K.S. and Ettefagh, M.M., "An experimental study on motion error of hexarot parallel manipulator", The International Journal of Advanced Manufacturing Technology,  Vol. 72, No. 9-12, (2014), 1361-1376.

16.   Bazoobandi, H., "Wavelet neural network with random wavelet function parameters", International Journal of Engineering-Transactions A: Basics,  Vol. 30, No. 10, (2017), 1510-1516.

17.   Hashemi, S.M.A., Haji Kazemi, H. and Karamodin, A., "Verification of an evolutionary-based wavelet neural network model for nonlinear function approximation", International Journal of Engineering,  Vol. 28, No. 10, (2015), 1423-1429.

18.   Huang, M. and Cui, B., "A novel learning algorithm for wavelet neural networks", ICNC 2005:Advances in Natural Computation,  Springer-Verlag Berlin Heidelberg, (2005), 421-421.

19.   Neshat, N., "An approach of artificial neural networks modeling based on fuzzy regression for‎ forecasting purposes", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 11, (2015), 1651-1655.

20.   Moody, J., Prediction risk and architecture selection for neural networks, in From statistics to neural networks. 1994, Springer.147-165.