Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator

Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.

• Thiang
• Handry Khoswanto
• Rendy Pangaldus

• Artificial neural network
• back propagation
• inverse kinematics
• manipulator
• robot.

[1] Dan W. Patterson. Artificial Neural Networks, Theory and Applications.
Singapore: Prentice Hall, 1996.
[2] Thiang, Rianto Chandra, Iwan Njoto Sandjaja, "One Arm Robot Position
Control Using Artificial Neural Network," in Proceedings of National
Seminar: The Application of Technology Toward Better Life.
Yogyakarta, 2005.
[3] Thiang, Indar Sugiarto, Hendrik Chandra, "DC Motor Speed Control
System Using Artificial Neural Network," in Proceedings of National
Seminar of Computer Science and Information Technology, Jakarta,
2004.
[4] Felix Pasila, "Multivariate Inputs for Electrical Load Forecasting on
Hybrid Neuro-Fuzzy and Fuzzy C-Means Forecaster," in Proceedings of
International Conference on Fuzzy Systems, Hongkong, 2008.
[5] Felix Pasila, Sautma Ronni, Thiang, Lie Hendra Wijaya, "Long-term
Forecasting in Financial Stock Market using Accelerated LMA on
Neuro-Fuzzy Structure and Additional Fuzzy C-Means Clustering for
Optimizing the GMFs," in Proceedings of International Joint
Conference on Neural Networks. Hongkong, 2008.
[6] Felix Pasila, "Neuro-Fuzzy Forecaster for Modeling and Forecasting
Electrical Load Competition Using Multivariate Inputs on Takagi-
Sugeno Networks," in Proceedings of International Conference on Soft
Computing, Intelligent System, and Information Technology. Bali, 2007.
[7] A.M.S., Zalzala and Morris, A.S., Neural Network For Robotic Control,
Theory And Application. London: Ellis Horwood, 1996.
[8] Craig, John J., Introduction to Robotics: Mechanics and Control. New
Jersey: Prentice Hall, 2005.
[9] 9. Martin T. Hagan, Neural Network Design. Boston: PWS Publishing,
1996.
[10] Zurada, J.M., Introduction To Artificial Neural Systems. Boston: PWS
Publishing, 1992.