Genetic-based fuzzy clustering for DC-motor friction identification and compensation

A fuzzy-logic-based model describing the friction present in a DC-motor system is derived. Based on fuzzy clustering techniques, the structure, as well as the premise and consequence parameters are inferred in an off-line manner. The fine-tuning of these parameters is accomplished through a genetic algorithm which minimizes a system modeling relevant functional. The genetic algorithm encodes these parameters as chromosomes, and creates the next generation of fuzzy models through natural selection and survival of the fittest chromosome. This model is used as a feedforward term for tracking purposes of the DC-motor's angular velocity. The proposed feedforward compensation scheme, coupled to a classical feedback controller improves the system's response in typical DC-motor micromaneuvers. Experimental results are offered to validate the performance of the proposed friction fuzzy model and the control technique.

[1]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Witold Pedrycz.  Identification in fuzzy systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  W. Pedrycz,et al.  Construction of fuzzy models through clustering techniques , 1993 .

[4]  M. B. Zarrop,et al.  Self-Tuning Systems: Control and Signal Processing , 1991 .

[5]  Carlos Canudas de Wit,et al.  Adaptive Friction Compensation in Robot Manipulators: Low Velocities , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[6]  Giovanni Muscato,et al.  Membership function shape and the dynamic behaviour of fuzzy systems , 1994 .

[7]  Dimitar Filev,et al.  Unified structure and parameter identification of fuzzy models , 1993, IEEE Trans. Syst. Man Cybern..

[8]  S. C. Southward,et al.  A Property of Stick-Slip Friction Models which Promotes Limit Cycle Generation , 1990, 1990 American Control Conference.

[9]  P. E. Wellstead,et al.  Knowledge-based self-adaptive control , 1988 .

[10]  Ching-Chang Wong,et al.  Switching-type fuzzy controller design by genetic algorithms , 1995, Fuzzy Sets Syst..

[11]  Carlos Canudas de Wit,et al.  Adaptive friction compensation in DC motor drives , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[12]  G. Langholz,et al.  Genetic-Based New Fuzzy Reasoning Models with Application to Fuzzy Control , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[13]  Bernard Friedland,et al.  On adaptive friction compensation , 1992 .

[14]  W. Pedrycz.  An identification algorithm in fuzzy relational systems , 1984 .

[15]  Carlos Canudas de Wit,et al.  A survey of models, analysis tools and compensation methods for the control of machines with friction , 1994, Autom..

[16]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Clark J. Radcliffe,et al.  Robust nonlinear stick-slip friction compensation , 1991 .

[18]  Brian Armstrong,et al.  Friction: experimental determination, modeling and compensation , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  J. Buckley,et al.  Fuzzy genetic algorithm and applications , 1994 .

[21]  Chen-Wei Xu,et al.  Fuzzy systems identification , 1989 .

[22]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[23]  R. Gorez,et al.  A fuzzy clustering method for the identification of fuzzy models for dynamic systems , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[24]  Brian Armstrong-Hélouvry,et al.  Stick-slip arising from Stribeck friction , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[25]  Raghu Krishnapuram,et al.  Fitting an unknown number of lines and planes to image data through compatible cluster merging , 1992, Pattern Recognit..

[26]  Brian Armstrong-Hélouvry,et al.  Control of machines with friction , 1991, The Kluwer international series in engineering and computer science.

[27]  Anthony Tzes,et al.  Neural network control for DC motor micromaneuvering , 1995, IEEE Trans. Ind. Electron..

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .