Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Automatica (Journal of IFAC)
General analytical structure of typical fuzzy controllers and their limiting structure theorems
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Sufficient conditions on general fuzzy systems as function approximators
Automatica (Journal of IFAC)
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Towards a paradigm for fuzzy logic control
Automatica (Journal of IFAC)
Approximation theory of fuzzy systems-MIMO case
IEEE Transactions on Fuzzy Systems
Multilayer discrete-time neural-net controller with guaranteed performance
IEEE Transactions on Neural Networks
Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Computers in Industry - Special issue: Soft computing in industrial applications
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
International Journal of Applied Mathematics and Computer Science
Discrete-Time adaptive controller design for robotic manipulators via neuro-fuzzy dynamic inversion
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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The objective of this paper is to achieve tracking control of a class of unknown feedback linearizable nonlinear dynamical systems using a discrete-time fuzzy logic controller (FLC). Discrete-time FLC design is significant because almost all FLCs are implemented on digital computers. A repeatable design algorithm and a stability proof for an adaptive fuzzy logic controller is presented that uses basis functions based on the fuzzy system, unlike most standard adaptive control approaches which generate basis vectors by computing a ''regression matrix''. A new approach to adapt the fuzzy system parameters is attempted. With mild assumptions on the class of discrete-time nonlinear systems, using this adaptive fuzzy logic controller the uniform ultimate boundedness of the closed-loop signals is shown under a persistency of excitation (PE) condition. New passivity properties of fuzzy logic systems are described. The result is a model-free universal fuzzy controller that works for any system in the given class of systems.