Abstract
An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.
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Althöfer, K. and Fraser, D. A.: Fuzzy obstacle avoidance for robotic manipulators, Neural Network World 6(2) (1996), 133-142.
Althöfer, K., Seneviratne, I. D., Zavlangas, P., and Krekelberg, B.: Fuzzy navigation for robotic manipulators, Internat. J. Uncertainty, Fuzziness and Knowledge-based Systems 6(2) (1998), 179-188.
Berghuis, H.: Model-based robot control: From theory to practice, PhD Thesis, Department of Electrical Engineering, University of Twente, Enschede, The Netherlands, June 1993.
Cheng, G. and Zhang, D.: Back-driving a truck with suboptimal distance trajectories: A fuzzy logic control approach, IEEE Trans. Fuzzy Systems 5 (1997), 369-380.
Connolly, C. I.: Harmonic functions as a basis for motor control and planning, PhD Dissertation, Department of Computer Science, University of Massachusetts, Amherst, MA, USA, 1994.
Ding, H. and Li, H. X.: Fuzzy avoidance control strategy for redundant manipulators, Engrg. Applications of Artificial Intelligence 12 (1999), 513-521.
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots, Internat. J. Robotics Res. 5 (1986), 90-98.
Kim, J.-O. and Khosla, P. K.: Real-time obstacle avoidance using harmonic potential functions, IEEE Trans. Robotics Automat. 8(3) (1992), 338-349.
Koditschek, D. E.: The control of natural motion in mechanical systems, J. Dyn. Systems Meas. Control 113 (1991), 547-551.
Koditschek, D. E.: Some applications of natural motion, J. Dyn. Systems Meas. Control 113 (1991), 552-557.
Lewis, F. L., Jagannathan, S., and Yesildirek, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor & Francis, London, 1999.
Mamdani, E. H. and Assilian, S.: Applications of fuzzy algorithms for control of simple dynamic plant, Proc. IEE 121 (1974), 1585-1588.
Mbede, J. B., Wei, W., Huang, X., and Wang, M.: Fuzzy sensor-based motion control among dynamic obstacles for intelligent rigid-link electrically driven arm manipulators, Revised for publication, in J. Intelligent Robotic Systems (2000).
Murray, R. M., Li, Z., and Sastry, S. S.: A Mathematical Introduction to Robotic Manipulation, CRC Press, Boca Raton, FL, 1994.
Pham, D. T. and Liu, X.: Identification of linear and nonlinear dynamic systems using recurrent neural networks, Artificial Intelligence Engrg. 8 (1993), 67-75.
Qu, Z. and Dawson, D. M.: Robust Tracking Control of Robot Manipulators, IEEE Press, New York, 1996.
Rimon, E. and Koditschek, D. E.: The construction of analytic diffeomorphisms for exact robot navigation on star worlds, in: Proc. of IEEE Conf. on Robotics Automation, 1989, pp. 21-26.
Rimon, E. and Koditschek, D. E.: Exact robot navigation using artificial potential functions, IEEE Trans. Robotics Automat. 8(5) (1992), 501-518.
Takagi, T. and Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Systems Man Cybernet. 15(1) (1985), 116-132.
Tarn, T.-J., Bejczy, A. K., Yun, X., and Li, Z.: Effect of motor dynamics on nonlinear feedback robot arm control, IEEE Trans. Robotics Automat. 7(1) (1991), 114-122.
Tsoukalas, L. H., Houstis, E. N., and Jones, G. V.: Neuro-fuzzy motion planner for intelligent robots, J. Intelligent Robotic Systems 19 (1997), 339-356.
Volpe, R. and Khosla, P. K.: Manipulator control with superquadric artificial potential functions: Theory and experiments, IEEE Trans. Systems Man Cybernet. 20(6) (1990), 1423-1436.
Zavlangas, P., Tzafestas, S., and Althoefer, K.: Navigation for robotic manipulators employing fuzzy logic, in: Proc. of the 3rd World Conf. on Integrated Design and Process Technology, Vol. 6, Berlin, Germany, July 6-9, 1998, pp. 278-283.
Zhang, D., Chen, G., and Malki, H. A.: Fuzzy-logic control of multi-link flexible-joint robotic manipulators, Internat. J. Intelligent Control Systems 2(1) (1998), 111-138.
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Mbede, J.B., Wei, W. & Zhang, Q. Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators. Journal of Intelligent and Robotic Systems 30, 155–177 (2001). https://doi.org/10.1023/A:1008194912825
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DOI: https://doi.org/10.1023/A:1008194912825