Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

An Introduction to Fuzzy Control

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.

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Type-2 fuzzy logic systems

Is there a need for fuzzy logic

We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system is then an accurate approximation of the original nonlinear system. Our approach to the analysis and design of observers for Takagi-Sugeno fuzzy systems is based on extending sliding mode observer schemes to the case of interpolated multiple local affine linear models. Thus, our main contribution is nonlinear observer analysis and design methods that can effectively deal with model/plant mismatches. Furthermore, we consider the difficult case when the weighting functions in the Takagi-Sugeno fuzzy system depend on the estimated state.

Observers for Takagi-Sugeno fuzzy systems

Model Based Fuzzy Control

An introduction to fuzzy control (2nd ed.)

Tutorials: A. Saffiotti: Fuzzy Logic in Autonomous Navigation.- D. Driankov: A Reminder of Fuzzy Logic.- Design of Individual Behaviors: A. Ollero, J. Ferruz, O. Sanchez, G. Heredia: Mobile Robot Path Tracking and Visual Target Tracking Using Fuzzy Logic.- R.R. Murphy: Fuzzy Logic for Fusion of Tactical Influences on Vehicle Speed Control.- J. Godjevac, N. Steele: Neuro-Fuzzy Control for Basic Mobile Robot Behaviours.- F. Hoffmann: The Role of Fuzzy Logic Control in Evolutionary Robotics.- Coordination of Behaviors: F.G. Pin, Y. Watanabe: Resolving Conflicts Between Behaviors Using Suppression and Inhibition.- S.G. Goodridge, M.G. Kay: Multi-Layered Fuzzy Behavior Fusion for Reactive Control of Autonomous Robots.- E.W. Tunstel Jr.: Fuzzy-Behavior Synthesis, Coordination, and Evolution in an Adaptive Behavior Hierarchy.- P. Pirjanian, M. Mataric: Multiple Objective vs. Fuzzy Behavior Coordination.- Mapping the Environment: E. Fabrizi, g. Oriolo, G. Ulivi: Accurate Map Building via Fusion of Laser and Ultrasonic Range Measures.- M. Lopez-Sanchez, R. Lopez de Mantaras, C. Sierra: Map Genaration by Co-operative Autonomous Robots Using Possibility Theory.- J. Gasos: Integrating Linguistic Descriptions and Sensor Observations for the Navigation of of Autonomous Robots.- Layer Integration: H. Surmann, L. Peters: MORIA: A Robot with Fuzzy Controlled Behaviour.- J. Zhang, A. Knoll: Integrating Deliberative and Reactive Strategies via Fuzzy Modular Control.

Dimiter Driankov

Papers

An Introduction to Fuzzy Control

Observers for Takagi-Sugeno fuzzy systems

Model Based Fuzzy Control

An introduction to fuzzy control (2nd ed.)

Fuzzy Logic Techniques for Autonomous Vehicle Navigation