Abstract
Recently, various control methods represented by proportional-integral-derivative (PID) control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.
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F. Nagata, K. Watanabe, T. Hase, Z. Haga, M. Omoto, K. Tsuda, O. Tsukamoto, M. Komino, Y. Kusumoto. Application of open architectural industrial robots and its simulation technique. International Journal of Computer Research, vol. 17, no. 1–2, pp. 1–40, 2008.
C. Chen, M. M. Trivedi, C. R. Bidlack. Simulation and animation of sensor-driven robots. IEEE Transactions on Robotics and Automation, vol. 10, no. 5, pp. 684–704, 1994.
F. Benimeli, V. Mata, F. Valero. A comparison between direct and indirect dynamic parameter identification methods in industrial robots. Robotica, vol. 24, no. 5, pp. 579–590, 2006.
F. Nagata. Simulation technique of velocity-based discretetime control system with intelligent control concepts for open architectural industrial robots. The Open Automation and Control Systems Journal, vol. 1, pp. 31–43, 2008.
J. Han, C. Moraga. The Influence of the sigmoid function parameters on the speed of backpropagation learning. Lecture Notes in Computer Science, Springer, vol. 930, pp. 195–201, 1995.
C. Torras. Robot adaptivity. Robotics and Autonomous Systems, vol. 15, no. 1–2, pp. 11–23, 1995.
A. T. Hasan, A. M. S. Hamouda, N. Ismail, H. M. A. A. Al-Assadi. An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F serial robot manipulator. Advances in Engineering Software, vol. 37, no. 7, pp. 432–438, 2006.
J. H. Horng. Hybrid MATLAB and LabVIEW with neural network to implement a SCADA system of AC servo motor. Advances in Engineering Software, vol. 39, no. 3, pp. 149–155, 2008.
S. N. Huang, K. K. Tana, T. H. Lee. Neural network learning algorithm for a class of interconnected nonlinear systems. Neurocomputing, vol. 72, no 4–6, pp. 1071–1077, 2009.
Y. Y. Wu, Y. Q. Wu. Stability analysis for recurrent neural networks with time-varying delay. International Journal of Automation and Computing, vol. 6, no. 3, pp. 223–227, 2009.
Y. Y. Wu, T. Li, Y. Q. Wu. Improved exponential stability criteria for recurrent neural networks with time-varying discrete and distributed delays. International Journal of Automation and Computing, vol. 7, no. 2, pp. 199–204, 2010.
R. P. Paul. Modeling, Trajectory Calculation and Servoing of a Computer Controlled Arm, Technical Report AIM-177, Artificial Intelligence Laboratory, Stanford University, USA, 1972.
J. J. Craig. Introduction to Robitics — Mechanics and Control, 2nd ed., Reading MA, USA: Addison Wesley Publishing Company, 1989.
P. Corke. A robotics toolbox for MATLAB. IEEE Robotics & Automation Magazine, vol. 3, no. 1, pp. 24–32, 1996.
P. Corke. MATLAB toolboxes: Robotics and vision for students and teachers. IEEE Robotics & Automation Magazine, vol. 14, no. 4, pp. 16–17, 2007.
F. Nagata, K. Kuribayashi, K. Kiguchi, K. Watanabe. Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers. In Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, IEEE, Jacksonville, USA, pp. 196–201, 2007.
M. Kawato. The feedback-error-learning neural network for supervised motor learning. Advanced Neural Computers, R. Eckmiller Ed., North-Holland, Holland: Elsevier, pp. 365–373, 1990.
J. Nakanishi, S. Schaal. Feedback error learning and nonlinear adaptive control. Neural Networks, vol. 17, no. 10, pp. 1453–1465, 2004.
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This work was supported by Grant-in-Aid for Scientific Research (C) (No. 20560248) of Japan.
Fusaomi Nagata received the B.Eng. degree from the Department of Electronic Engineering at Kyushu Institute of Technology, Japan in 1985, and the D. Eng. degree from the Faculty of Engineering Systems and Technology at Saga University, Japan in 1999. He was a research engineer with Kyushu Matsushita Electric Co., Japan from 1985 to 1988, and a special researcher with Fukuoka Industrial Technology Center, Japan from 1988 to 2006. He is currently an associate professor at the Department of Mechanical Engineering, Faculty of Engineering, Tokyo University of Science, Yamaguchi, Japan.
His research interests include intelligent control of industrial robot and its applications.
Keigo Watanabe received the B.Eng. and M.Eng. degrees in mechanical engineering from the University of Tokushima, Japan in 1976 and 1978, respectively, and the D.Eng. degree in aeronautical engineering from Kyushu University, Japan in 1984. From 1980 to 1985, he was a research associate at Kyushu University. From 1985 to 1990, he was an associate professor at the College of Engineering, Shizuoka University, Japan. From April 1990 to March 1993, he was an associate professor, and from April 1993 to March 1998, he was a full professor in the Department of Mechanical Engineering at Saga University, Japan. From April 1998, he was with the Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University. Currently, he is with the Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University, Japan.
His research interests include stochastic adaptive estimation and control, robust control, neural network control, fuzzy control, genetic algorithms and their applications to the robotic control.
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Nagata, F., Watanabe, K. Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot. Int. J. Autom. Comput. 8, 54–61 (2011). https://doi.org/10.1007/s11633-010-0554-0
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DOI: https://doi.org/10.1007/s11633-010-0554-0