In this paper an attempt is made to bring together two independently pursued research topics viz., (1) type I fuzzy modeling and (2) sensor fault tolerant adaptive control. First a brief but succinct review on type I fuzzy modeling is carried out. The same is presented under the following heads, viz., (1) recent techniques in fuzzy modeling (2) fuzzy clustering techniques (3) parametric classes of operators and de-fuzzification and (4) rule base reduction techniques. Then the trends in adaptive control and sensor fault diagnosis are discussed at length. Necessity for integrated sensor fault tolerant adaptive control is then put forth. Then, a general framework in the form of signal flow block diagram for sensor fault tolerant adaptive control is presented. In particular, possibility of extending fuzzy modeling techniques for various blocks involved in the proposed general framework of sensor fault tolerant adaptive control is explored with clarity.
[...] Magne Setnes extended the standard G-K algorithm by introducing Gram-Schmidt orthogonal decomposition based least square estimation of consequent parameters which has a feature of selecting most influential clusters in the fuzzy modeling process, and hence the ability to reduce the number of rules in the developed TSK fuzzy model. The least square estimation technique mentioned above expects the user to specify a threshold value which affects reduction of rules in the process of cluster formation. Stopping criteria for the above iterative algorithm is also formulated. [...]
[...] The Combs method gives good scalability in that as the number of inputs in the fuzzy decision system increases, the rules increase linearly but not exponentially, thereby reducing the number of rules in the resulting fuzzy system ADAPTIVE CONTROL AND SENSOR FAULT DIAGNOSIS Most of the real world systems are nonlinear parameter varying systems. Parameters may vary over time or as system operating point changes or both. Adaptive system control is a process of system control which incorporates automatic controller tuning depending on parameter changes. [...]
[...] Further consideration of highly nonlinear dynamic systems and testing the developed sensor fault tolerant adaptive control techniques gives a greater credibility for the proposed framework of an all fuzzy sensor fault tolerant adaptive control system REFERENCES 1. John Yen and Reza Langari, “Fuzzy Logic: Intelligence, Control and Information”, Pearson Education H. J. Zimmermann, “Fuzzy Set Theory and its Applications”, Kluwer Academic Publishers Michio Sugeno and Takahiro Yasukawa, FuzzyLogic-Based Approach to Qualitative Modeling”, IEEE Trans. on Fuzzy Syst., vol pp. 7-31, Feb J. [...]
[...] 288-299, Aug Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, Wiley D Driankov, H Hellendoorn and M Reinfrank, Introduction to Fuzzy Control”, Narosa Publishing House, India U. Kiencke and L. Nielsen, “Automotive Control Systems for Engine, Driveline and Vehicle”, 2nd Edition, Springer Fang Liu, “Data-Based Fault Detection and Isolation (FDI) Methods for Nonlinear Ship Propulsion System”, Master of Applied Science in Engineering Science, Thesis,© Fang Liu, Simon Fraser University, Burnaby, BC, Canada Simon Oblak, “Interval fuzzy modelling in fault detection for [...]
[...] Thus an integrated fuzzy adaptive control and sensor fault tolerant scheme is expected to provide a good opportunity for developing and testing adaptive fuzzy systems and at the same time a possibility of resulting in sound benchmark problems for the research community in fuzzy modeling SENSOR FAULT TOLERANT ADAPTIVE CONTROL BASED ON FUZZY MODELING TECHNIQUES From the discussions carried out in section III we can conclude that a general acceptable standard framework is required for the sensor fault tolerant technology standardization. [...]
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