Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Sets and Systems - Theme: Fuzzy control
Information Sciences: an International Journal
Journal of Intelligent and Robotic Systems
An improved robust adaptive fuzzy controller for MIMO systems
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Sugeno based robust adaptive fuzzy sliding mode controller for SISO nonlinear systems
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Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system
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Extension of fuzzy adaptive laws to IT2 fuzzy systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Novel direct adaptive fuzzy control system design
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Fuzzy control for seismically excited bridges with sliding bearing isolation
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Robust L2-gain compensative control for direct-adaptive fuzzy-control-system design
IEEE Transactions on Fuzzy Systems
Indirect adaptive fuzzy and impulsive control of nonlinear systems
International Journal of Automation and Computing
DSC approach to robust adaptive NN tracking control for a class of SISO systems
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Machine Vision and Applications
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Stable adaptive fuzzy control is a self-tuning concept for fuzzy controllers that uses a Lyapunov-based learning algorithm, thus guaranteeing stability of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal. In the paper, two new methods for stable adaptive fuzzy control are presented. The first method is an extension of an existing concept: it is shown that a major drawback of that concept, the necessity for new adaptation at every change of the reference signal, can be avoided by a simple modification. The main focus of the paper is on the presentation of a second method, which extends the applicability of stable adaptive fuzzy control to a broader class of nonlinear plants; this is achieved by an improved controller structure adopted from the neural network domain. Performance and limitations of the proposed methods, as well as some practical design aspects, are discussed and illustrated with simulation results