Genetically generated double-level fuzzy controller with a fuzzy adjustment strategy
Authors: 
Sofiane Achiche, Wei Wang, Zhun Fan, Ali Ozkil, Torben Sorensen, Jiachuan Wang, Erik GoodmanAuthors Info & Claims
Sofiane Achiche, Wei Wang, Zhun Fan, Ali Ozkil, Torben Sorensen, Jiachuan Wang, Erik GoodmanAuthors Info & ClaimsPages 1880 - 1887
Abstract
This paper describes the use of a genetic algorithm (GA) in tuning a double-level modular fuzzy logic controller (DLMFLC), which can expand its control working zone to a larger spectrum than a single-level FLC. The first-level FLCs are tuned by a GA so that the input parameters of their membership functions and fuzzy rules are optimized according to their individual working zones. The second-level FLC is then used to adjust contributions of the first-level FLCs to the final output signal of the whole controller, i.e., DLMFLC, so that it can function in a wider spectrum covering all individual working zones of the first-level FLCs. The second-level FLC is again optimized by a GA. An inverted pendulum system (IPS) is used to demonstrate the feasibility of the approach.
References
[1]
Verbruggen, H.B. and Bruijn, P.M., Fuzzy control and conventional control: What is (And Can Be) the Real Contribution of Fuzzy Systems? Fuzzy Sets Systems, Vol. 90, 151--160, 1997.
[2]
Kowalska, T.O., Szabat, K. and Jaszczak, K., The Influence of Parameters and Structure of PI--Type Fuzzy--Logic Controller on DC Drive System Dynamics, Fuzzy Sets and Sysems, Vol. 131, 251--264, 2002.
[3]
Ahmed, M.S., Bhatti, U.L., Al-Sunni, F.M. and El-Shafei, M., Design of a Fuzzy Servo--Controller, Fuzzy Sets and Systems, vol. 124, 231--247, 2001.
[4]
Zilouchian, A., Juliano, M., Healy, T. and Davis, J., Design of Fuzzy Logic Controller for a Jet Engine Fuel System, Control and Engineering Practices, Vol. 8, 873--883, 2000.
[5]
Zadeh, L.A., Fuzzy sets, Information Control, Vol. 8, pp. 339--353, 1965.
[6]
Liu, B-D., Design and Implementation of the Tree-Based Fuzzy Logic Controller, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics., Vol.27, No.3, 475--487, 1997.
[7]
Zhiqiang, G., A Stable Self-Tuning Fuzzy Logic Control System for Industrial Temperature Regulation, IEEE Transactions on Industry Applications.Vol.38, No.2, 414--424, 2002.
[8]
Shapiro, A.F., Fuzzy Logic in Insurance, Insurance: Mathematics and Economics, Vol.35, No.2, 399--424, 2004.
[9]
Hayward, G. and Davidson, V., Fuzzy Logic Applications, Analyst, Vol.128, 1304--1306, 2003.
[10]
Peri, V.M. and Simon, D., Fuzzy Logic Control for an Autonomous Robot, North American Fuzzy Information Processing Society, NAFIPS 2005 Annual Meeting, 337--342, 2005.
[11]
Castellano, G., Attolico, G. and Distante, A., Automatic Generation of Fuzzy Rules for Reactive Robot Controllers, Robotics and Autonomous Systems, Vol. 22, 133--149, 1997.
[12]
Higgins, C. M. and Goodman, R. M., Fuzzy Rule--Based Networks for Control, IEEE Transactions on Fuzzy Systems, Vol. 2, No. 1, 82--88, 1994.
[13]
Wang, L. X. and Mendel, J. M., Generating Fuzzy Rules By Learning From Examples, IEEE Transactions on Systems, Man and Cybernetics, Vol. 22, No. 6, 1414--1427, 1992.
[14]
Mohamed, A.M.O., Neuro-Fuzzy Logic Model for Evaluating Water Content of Sandy Soils, Computer--Aided Civil and Infrastructure Engineer, Vol.19, No.4,274--287, 2004.
[15]
Horng, J.H., SCADA System of DC Motor with Implementation of Fuzzy Logic Controller on Neural Network, Advances in Engineering Software, Vol.33 Issue.6, pp:361--364, 2002.
[16]
Van Cleave, D.W., Tuning of Proportional Plus Derivative Fuzzy Logic Controller using Neural Network, Proceedings of the 33rd Southeastern Symposium on System Theory, 365 --370, 2001.
[17]
Nandi, A.K. and Pratihar, D.K, Automatic Design of Fuzzy Logic Controller using a Genetic Algorithm - To Predict Power Requirement and Surface Finish in Grinding, Journal of Material Processing Technology, Vol. 148, 288--300, 2004.
[18]
Achiche, S., Baron, L. and Balazinski, M., Predictive Fuzzy Control of Paper Quality, Annual Meeting of the North American Fuzzy Information Processing Society, CD--ROM Version, 2006.
[19]
Chiang, C.K., A Self-Learning Fuzzy Logic Controller Using Genetic Algorithms with Reinforcements, IEEE Transactions on Fuzzy Systems, Vol.5, No.3, 460--467, 1997.
[20]
Chin, T.C., Genetic Algorithms for Learning the Rule Base of Fuzzy Logic Controller, Fuzzy Sets and Systems, Vol.97, No.1, 1--7, 1998.
[21]
Arslan, A and Kaya, M., Determination of Fuzzy Logic Membership Functions using Genetic Algorithms: Application to Structure--Odor Modeling, Fuzzy Sets and Systems, Vol.118, No.2, 297--306, 2001.
[22]
Li, R. and Zhang Y., Fuzzy Logic Controller Based on Genetic Algorithms. Fuzzy Sets and Systems, Vol.83, No.1, 1--10, 1996.
[23]
Liu, B.D., Design of Adaptive Fuzzy Logic Controller Based on Linguistic--Hedge Concepts and Genetic Algorithms, IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol.31, No.1, 32 --53, 2001.
[24]
Chung, H.Y. and Chiang, C.K., A Self-Learning And Tuning Fuzzy Logic Controller Based on Genetic Algorithms and Reinforcements, International Journal of Intelligent Systems, Vol.12, Issue.9, 673--694, 1997.
[25]
Balazinski, M., Achiche, S.and Baron, L. Influence of Optimization and Selection Criteria on Genetically--Generated Fuzzy Knowledge Bases, 2nd International Conference on Advanced Manufacturing Technology, 159--164, 2000.
[26]
Yuhui, S., Eberhart, R. and Yaobln C., Evolutionary Modular Fuzzy System, IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, 387--391, 1998.
[27]
Zadeh, L.A., Outline of New Approach to the Analysis of Complex Systems and Decisions Processes, IEEE Transactions of Systems, Man and Cybernetics, Vol.3, 28--44, 1973.
[28]
Klir, G.J., Zadeh, L. A., Yuan, B., Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, World Scientific Series, 1996.
[29]
Baron, L., Achiche, S., Balazinski, M., Fuzzy Decision Support System Knowledge Base Generation Using a Genetic Algorithm. International Journal of Approximate Reasoning, Vol.28, No. 2--3, 125--148, 2001.
[30]
Young J.P., Hyung, S.C. and Dong H.Ch., Genetic Algorithm-Based Optimization Of Fuzzy Logic Controller Using Characteristic Parameters, IEEE International Conference on Evolutionary Computation, Vol.2, 831--836, 1995.
[31]
Houck, C.R., Joines, J. and Kay, K., A Genetic Algorithm for Function Optimization: A Matlab implementation, ACM Transactions on Mathematical Software, http://www.eos.ncsu.edu/eos/service/ie/research/kay_res/GAToolBox/gaot, 1996.
[32]
Lee, M.A., Takagi, Integrating Design States of Fuzzy System using Genetic Algorithms, Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, San Francisco, 612--617, 1993.
[33]
Foran, J., Optimization of a Fuzzy Logic Controller Using Genetic Algorithms, Master in Engineering Report, 108p, 2002.
Index Terms
- Genetically generated double-level fuzzy controller with a fuzzy adjustment strategy
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Published: 07 July 2007
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