Fuzzy Relational Control of an Uncertain System

The paper considers the usefulness of a control strategy based on a fuzzy relational model of the controller to counteract uncertainties caused by measurement noise and unmeasured disturbances. The fuzzy relational model is identified using a combination of feedback error learning and fuzzy identification. An important feature of the resulting fuzzy relational model is that it will generate a fuzzy output in the presence of uncertainties. Two causes of uncertainty are considered separately, the first cause of uncertainty is due to the noise on the sensor measuring the controlled variable and the second one is an unmeasured input disturbance. Results are presented that show that the fuzzy control signal is representative of the uncertainties and that conditional defuzzification can then be used to improve the control performance by reducing the control activity.

[1]  Gang Feng,et al.  Robust control for a class of uncertain nonlinear systems: adaptive fuzzy approach based on backstepping , 2005, Fuzzy Sets Syst..

[2]  Yongming Li,et al.  Adaptive fuzzy backstepping robust control for uncertain nonlinear systems based on small-gain approach , 2010, Fuzzy Sets Syst..

[3]  Kevin M. Passino,et al.  Training fuzzy systems to perform estimation and identification , 1995 .

[4]  Shaocheng Tong,et al.  A Combined Backstepping and Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control , 2009, IEEE Transactions on Fuzzy Systems.

[5]  Aldo Cipriano,et al.  Fuzzy predictive control of a solar power plant , 2005, IEEE Transactions on Fuzzy Systems.

[6]  Yansheng Yang.  Direct robust adaptive fuzzy control (DRAFC) for uncertain nonlinear systems using small gain theorem , 2005, Fuzzy Sets Syst..

[7]  M. Kawato,et al.  Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[8]  João Miguel da Costa Sousa,et al.  Fault tolerant control using a fuzzy predictive approach , 2012, Expert Syst. Appl..

[9]  Mitsuo Kawato,et al.  Neural network control for a closed-loop System using Feedback-error-learning , 1993, Neural Networks.

[10]  Christoph Sing,et al.  Identification of Fuzzy Relational Models from Unevenly Distributed Data Using Optimization Methods , 2000 .

[11]  J. N. Ridley,et al.  Probabilistic fuzzy model for dynamic systems , 1988 .

[12]  Woei Wan Tan,et al.  A self-learning fuzzy controller for embedded applications , 2000, Autom..

[13]  Uzay Kaymak,et al.  Model predictive control using fuzzy decision functions , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Uzay Kaymak,et al.  Weighting goals and constraints in fuzzy predictive control , 2006, J. Intell. Fuzzy Syst..

[15]  Gang Feng,et al.  A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems , 2004, IEEE Trans. Syst. Man Cybern. Part A.

[16]  Héctor Pomares,et al.  Online global learning in direct fuzzy controllers , 2004, IEEE Transactions on Fuzzy Systems.

[17]  Khaled Belarbi,et al.  A Stable Model-Based Fuzzy Predictive Control Based on Fuzzy Dynamic Programming , 2007, IEEE Transactions on Fuzzy Systems.

[18]  Arthur L. Dexter,et al.  A fuzzy decision-making approach to temperature control in air-conditioning systems , 2005 .

[19]  Plamen P. Angelov,et al.  An approach for fuzzy rule-base adaptation using on-line clustering , 2004, Int. J. Approx. Reason..

[20]  Shaocheng Tong,et al.  Observer-based fuzzy adaptive control for strict-feedback nonlinear systems , 2009, Fuzzy Sets Syst..

[21]  Tong Heng Lee,et al.  Adaptive and robust controller design for uncertain nonlinear systems via fuzzy modeling approach , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Ferenc Szeifert,et al.  Inverse fuzzy-process-model based direct adaptive control , 1999 .

[23]  Shaocheng Tong,et al.  Adaptive fuzzy output tracking control for a class of uncertain nonlinear systems , 2009, Fuzzy Sets Syst..

[24]  Xiaogang Ruan,et al.  On-line adaptive control for inverted pendulum balancing based on feedback-error-learning , 2007, Neurocomputing.

[25]  Rong-Jong Wai,et al.  Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System , 2008, IEEE Transactions on Neural Networks.

[26]  Junsheng Ren,et al.  Adaptive fuzzy robust tracking controller design via small gain approach and its application , 2003, IEEE Trans. Fuzzy Syst..