Abstract
The results obtained by a rule-based proportional, integral, derivative (PID) precompensator controller applied to a two-joint manipulator are discussed. The end effector is made to follow a specified trajectory obtained from the inverse kinematics by an appropriate design of a fuzzy control law. The desired trajectory is determined by the values of the joint variables and the structural kinematics parameters of the manipulator. The performance of the PID controller is exploited here to build a fuzzy precompensator that will enhance the conventional PID and to obtain better performances and results. The fuzzy rule base of the precompensator designed is found by associating two evolutionary algorithms that search for the optimal solution.
Similar content being viewed by others
References
Abdessemed, F. and Benmahammed, K.: A two-layer robot controller design, in: World Congress WCCI'98 on Computational Intelligence, Anchorage Alaska, Proc. of the 1998 IEEE Internat. Conf. on Fuzzy Systems, Vol. 1, May 4–9, 1998, pp. 522–527.
Abe, S. and Lan, M. S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification, IEEE Trans. Fuzzy Systems 3(1995), 18–28.
Amestegui, M., Ortega, R., and Ibarra, J. M.: Adaptive linearizing decoupling robot control: A comparative study of different parametrizations, in: Proc. of the 5th Yale Workshop on Applications of Adaptive Systems Theory, New Haven, CT, 1987.
Bach, T., Hammel, U., and Shwefel, H. P.: Evolutionary computation: Comments on the history and current state, IEEE Trans. Evolutionary Comput. 1(1) (1997).
Balestrino, A., DeMaria, G., and Sciavicco, L.: An adaptive model following control for robotic manipulators, J. Dyn. Systems Measm. Control 105(1983), 143–151.
Bejczy, A. K.: Robot arm dynamics and control, Technical Memorandum 33–669, JET propulsion laboratory, 1974.
Cela, A., Hammam, Y., and Carrière, A.: Robust fuzzy logic controller for trajectory tracking of robotic systems, in: IEEE Internat. Conf. on Systems Man and Cybernetics, 1994, pp. 459–464.
Craig, J. J., Hsu, P. I., and Sastry, S.: Adaptive control of mechanical manipulators, in: IEEE Internat. Conf. on Robotics and Automation, San Francisco, CA, 1986.
DeJong, K. A.: An analysis of the behavior of a class of genetic adaptive systems, PhD Dissertation, University of Michigan, Ann Arbors, 1975.
Dubowsky, S. and Desforges, D. T.: The application of model reference adaptive control for robotic manipulators, ASME J. Dyn. Systems Measm. Control 101(1979), 193–208.
Goldberg, D. E.: Genetic Algorithm in Search, Optimization and Machine Learning, Adison-Wesley, Reading, MA, 1989.
Holland, J. H.: Outline for a logical theory of adaptive systems, J. Assoc. Comput. Mach. 3(1962), 277–314.
Hsia, T. C.: Adaptive control of robot manipulators: A review, in: IEEE Internat. Conf. on Robotics and Automation, San Francisco, CA, 1986.
Jin, Y.: Decentralized adaptive fuzzy control of robot manipulator, IEEE. Trans. Systems Man Cybernet. 28(1) (1998), 47–57.
Karr, C. L.: Design of an adaptive fuzzy logic controller using a genetic algorithm, in: Proc. of the 4th Internat. Conf. on Genetic Algorithms, San Diego, July 13–16, 1991, pp. 450–457.
Kawato, M., Uno, Y., Isobe, M., and Suzuki, R.: Hierarchical neural network model for voluntary movement with application to robotics, IEEE Control Systems Mag.(1998), 8–16.
Kelly, R. and Carelli, R.: Input-output analysis of an adaptive computed torque plus compensation control for manipulators, in: 27th IEEE Conf. on Decision and Control, Austin, TX, 1988.
Khalil, W.: Contribution á la commande automatique des manipulateurs avec l'aide d'un modèle mathématique des mécanismes, Thèse d'État USTL Montpelier, 1978.
Kim, H., Kim, K-C., and Chong, E. K. P.: Fuzzy precompensated PID controllers, IEEE Trans. Control Systems Techn. 2(4) (1994).
King, P. J. and Mamdani, E. H.: The application of fuzzy control systems to industrial processes, Automatica 13(1976), 235–242.
Koivo, A. J. and Guo, T. H.: Control of robotic manipulator with adaptive controller, in: 20th IEEE Conf. on Decision and Control, San Diego, CA, December 1981, pp. 271–276.
Landau, I. D. and Horowitz, R.: Synthesis of adaptive controller for robot manipulators using a passive feedback systems approach, in: Proc IEEE Conf. on Robotics and Automation, Philadelphia, 1988.
Landau, I. D. and Horowitz, R.: Application of the passive systems approach to the stability analysis of adaptive controllers for robot manipulators, Internat. J. Adaptive Control Signal Processing 3(1989).
Lee, C. C.: Fuzzy logic in control systems: Fuzzy logic controller: Part I-II, IEEE Trans. Systems Man Cybernet. 20(2) (1990).
Lee, C. S. G. and Chung, M. J.: An adaptive control strategy for computer-based manipulators, IEEE(1982).
Lee, M. A. and Takagi, H.: Integrating design stages of fuzzy systems using genetic algorithms, in: Proc. of the 2nd IEEE Internat. Conf. on Fuzzy Systems, New York, 1993, pp. 612–617.
Lozano, R. and Brogliato, B.: Adaptive control of robot manipulators with flexible joints, IEEE(1989).
Mamdani, E. H.: Application of fuzzy algorithms in control of simple dynamic plant, Proc. IEE 121(12) (1974), 1585–1588.
Mandic, N. J., Scharf, E. M., and Mamdani, E. H.: Rule-based controller to the dynamic control of a robot arm, IEE Proc. 132(4) (1985).
Meslin, J. M., Zhou, J., and Coiffet, P.: Fuzzy dynamic control of manipulators: A scheduling approach, in: Internat. Conf. on Systems, Man and Cybernetics(IEEE SMC'93), 1993.
M'saad, M. and Sanchez, G.: Partial state reference model adaptive control of multivariable systems, Automatica 28(6) 1189–1197.
Ozaki, T., Suzuki, T. Furuhashi, T., Okuma, S., and Ushikawa, Y.: Trajectory control of robotic manipulators using neural networks, IEEE Trans. Industr. Electronics 38(3) (1991), 195–202.
Procyk, T. J. and Mamdani, E. H.: A linguistic self-organizing process control, Automatica 18(1979), 15–30.
Sadegh, N. and Horowitz, R.: Stability analysis of an adaptive controller for robotic manipulators, in: IEEE Internat. Conf. on Robotics and Automation, Raleigh, NC, 1987.
Seraji, H.: Linear multivariable control of two link robots, J. Robotic Systems 3(4) (1986), 349–365.
Seraji, H.: Direct adaptive control of manipulators in Cartesian space, J. Robotic Systems(1987).
Singh, S. N.: Adaptive model following control of nonlinear robotic systems, IEEE Trans. Automat. Control 30(11) (1985), 1099–1100.
Slotine, J. and Li, W.: Adaptive robot control: A new perspective, Proc. IEEE J. Robotics Automat. 3(4) (1987), 345–351.
Spong, M. W. and Ortega, R.: On adaptive inverse dynamics control of rigid robots, IEEE Trans. Automat.(1988).
Sugeno, M.: Industrial Application of Fuzzy Control, Elsevier Science, Netherlands, 1985.
Tay, T. T. and Tan, S.W.: Fuzzy systems as parameter estimator of nonlinear dynamic function, IEEE Trans. Systems Man Cybernet.B 27(2) (1997).
Wang, L. X.: Stable adaptive fuzzy control of nonlinear systems, IEEE Trans. Fuzzy Systems 1(2) (1993).
Whiteney, D. E.: Resolved motion rate control of manipulators and human protheses, IEEE Trans. Man Machine Systems 10(2) (1969), 47–53.
Zadeh, L. A.: Outline of a new approach to the analysis of complex systems and decisions, IEEE Trans. SMG 3(1973), 28–44.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Abdessemed, F., Benmahammed, K. A Two-Layer Robot Controller Design Using Evolutionary Algorithms. Journal of Intelligent and Robotic Systems 30, 73–94 (2001). https://doi.org/10.1023/A:1008122728715
Issue Date:
DOI: https://doi.org/10.1023/A:1008122728715