On Enhancing Robustness of an Evolutionary Fuzzy Tracking Controller
This paper presents three-phase evolution search methodology to automatically design fuzzy logic controllers (FLCs) that can work in a wide range of operating conditions. These include varying load, parameter variations, and unknown external disturbances. The three-phase scheme consists of an exploration phase, an exploitation phase and a robustness phase. The first two phases search for FLC with high accuracy performances while the last phase aims at obtaining FLC providing the best compromise between the accuracy and robustness performances. Simulations were performed for direct-drive two-axis robot arm. The evolved FLC with the proposed design technique found to provide a very satisfactory performance under the wide range of operation conditions and to overcome problem associated with coupling and nonlinearities characteristics inherent to robot arms.
[1] P. J. C. Branco and J. A. Dente, "An experiment in automatic modeling
an electrical drive system using fuzzy logic," IEEE Trans. Syst. Man
Cybern. C, vol. 28, pp. 254-262, May 1998.
[2] G. C. Mouzouris and J. M. Mendel, "Dynamic nonsingleton fuzzy logic
systems for nonlinear modeling," IEEE Trans. Fuzzy Syst., vol. 5, no. 2,
pp. 199-208, 1997.
[3] B. Forouraghi, "A genetic algorithm for multiobjective robust
design,".Applied Intelligence, no. 12, pp. 151-161, 2000.
[4] S. Tsutsui, and A. Ghosh. "Genetic algorithms with a robust solution
searching scheme, " IEEE Transactions on Evolutionary Computation,
Vol.1, No. 3, pp. 201-208, 1997.
[5] K. Hacker., J. Eddy, and K. Lewis, "Efficient Global Optimization Using
Hybrid Genetic Algorithms," Proceedings of the 9th
AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis
and Optimization, Atlanta, Georgia,, 2002.
[6] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning. Reading, MA: Addison-Wesley, 1991.
[7] H. Megherbi, A. C. Megherbi, K. Benmahammed and A. Hamzaoui,
"Design of smooth fuzzy controller for robot manipulators using biphase
integer-coded genetic algorithm, " The seventh International
Symposium on Programming and Systems, May 9-11 2005, Algers,
Algeria, pp. 133-144.
[8] Goldberg, D. E. (), Real-coded genetic algorithms, virtual alphabets, and
blocking, Complex Systems, 5, pp. 139-67, 1991.
[9] K.C. Ng and Y. Li, "Design of Sophisticated Fuzzy Logic Controllers
using Genetic Algorithms", IEEE World Congress on Computational
Intelligence, In Proc. 3rd IEEE Int. Conf. On Fuzzy Systems, Orlando,
FL, June 1994, Vol. 3, pp. 1708-1712.
[10] Parkinson, A., "Robust Mechanical Design Using Engineering Models",
Journal of Mechanical Design, Vol. 117, pp. 48-54, 1995.
[11] Hacker, K., "Efficient Robust Systems Design through the Use of
Hybrid Optimization and Distributed Computing", Ph.D. Dissertation,
University at Buffalo., 2002.
[12] Kouvelis, P. and G. Yu, "Robust Discrete Optimization and its
Applications". Kluwer Academic Publishers, Netherlands, 1997.
[13] Bonnans and A. Shapiro, "Optimization problems with perturbations, a
guide tour". SIAM Review, Vol 40, pp. 202-227, 1998.
[14] Wang L.X. and J. M. Mendel, "Fuzzy Basis Function, Universal
Approximation, and Orthogonal Least-Squares Learning. IEEE Trans.
neural networks, Vol. 3, No. 5, 1992.
[1] P. J. C. Branco and J. A. Dente, "An experiment in automatic modeling
an electrical drive system using fuzzy logic," IEEE Trans. Syst. Man
Cybern. C, vol. 28, pp. 254-262, May 1998.
[2] G. C. Mouzouris and J. M. Mendel, "Dynamic nonsingleton fuzzy logic
systems for nonlinear modeling," IEEE Trans. Fuzzy Syst., vol. 5, no. 2,
pp. 199-208, 1997.
[3] B. Forouraghi, "A genetic algorithm for multiobjective robust
design,".Applied Intelligence, no. 12, pp. 151-161, 2000.
[4] S. Tsutsui, and A. Ghosh. "Genetic algorithms with a robust solution
searching scheme, " IEEE Transactions on Evolutionary Computation,
Vol.1, No. 3, pp. 201-208, 1997.
[5] K. Hacker., J. Eddy, and K. Lewis, "Efficient Global Optimization Using
Hybrid Genetic Algorithms," Proceedings of the 9th
AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis
and Optimization, Atlanta, Georgia,, 2002.
[6] D. E. Goldberg, Genetic Algorithms in Search, Optimization and
Machine Learning. Reading, MA: Addison-Wesley, 1991.
[7] H. Megherbi, A. C. Megherbi, K. Benmahammed and A. Hamzaoui,
"Design of smooth fuzzy controller for robot manipulators using biphase
integer-coded genetic algorithm, " The seventh International
Symposium on Programming and Systems, May 9-11 2005, Algers,
Algeria, pp. 133-144.
[8] Goldberg, D. E. (), Real-coded genetic algorithms, virtual alphabets, and
blocking, Complex Systems, 5, pp. 139-67, 1991.
[9] K.C. Ng and Y. Li, "Design of Sophisticated Fuzzy Logic Controllers
using Genetic Algorithms", IEEE World Congress on Computational
Intelligence, In Proc. 3rd IEEE Int. Conf. On Fuzzy Systems, Orlando,
FL, June 1994, Vol. 3, pp. 1708-1712.
[10] Parkinson, A., "Robust Mechanical Design Using Engineering Models",
Journal of Mechanical Design, Vol. 117, pp. 48-54, 1995.
[11] Hacker, K., "Efficient Robust Systems Design through the Use of
Hybrid Optimization and Distributed Computing", Ph.D. Dissertation,
University at Buffalo., 2002.
[12] Kouvelis, P. and G. Yu, "Robust Discrete Optimization and its
Applications". Kluwer Academic Publishers, Netherlands, 1997.
[13] Bonnans and A. Shapiro, "Optimization problems with perturbations, a
guide tour". SIAM Review, Vol 40, pp. 202-227, 1998.
[14] Wang L.X. and J. M. Mendel, "Fuzzy Basis Function, Universal
Approximation, and Orthogonal Least-Squares Learning. IEEE Trans.
neural networks, Vol. 3, No. 5, 1992.
@article{"International Journal of Electrical, Electronic and Communication Sciences:63857", author = "H. Megherbi and A. C. Megherbi and N. Megherbi and K. Benmahamed", title = "On Enhancing Robustness of an Evolutionary Fuzzy Tracking Controller", abstract = "This paper presents three-phase evolution search methodology to automatically design fuzzy logic controllers (FLCs) that can work in a wide range of operating conditions. These include varying load, parameter variations, and unknown external disturbances. The three-phase scheme consists of an exploration phase, an exploitation phase and a robustness phase. The first two phases search for FLC with high accuracy performances while the last phase aims at obtaining FLC providing the best compromise between the accuracy and robustness performances. Simulations were performed for direct-drive two-axis robot arm. The evolved FLC with the proposed design technique found to provide a very satisfactory performance under the wide range of operation conditions and to overcome problem associated with coupling and nonlinearities characteristics inherent to robot arms.
", keywords = "Fuzzy logic control, evolutionary algorithms, robustness, exploration/exploitation phase.", volume = "1", number = "7", pages = "1059-8", }
