Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Sets and Systems - Theme: Fuzzy control
Design and Implementation of Fuzzy Sliding-Mode Controller for a Wedge Balancing System
Journal of Intelligent and Robotic Systems
A study on hybrid random signal-based learning and its applications
International Journal of Systems Science
Sugeno based robust adaptive fuzzy sliding mode controller for SISO nonlinear systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
International Journal of Computer Applications in Technology
Adaptive fuzzy control of a non-linear servo-drive: Theory and experimental results
Engineering Applications of Artificial Intelligence
Adaptive fuzzy tracking control of nonlinear systems
WSEAS Transactions on Systems and Control
Fundamentals of a fuzzy-logic-based generalized theory of stability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system
Fuzzy Sets and Systems
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
Adaptive fuzzy H∞tracking control for a class of uncertain nonlinear systems based on LMI technique
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Adaptive control using interval type-2 fuzzy logic
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design
Information Sciences: an International Journal
The design of fuzzy controller by means of genetic algorithms and NFN-based estimation technique
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Fuzzy supervisor for combining sliding mode control and H∞ control
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
A reference model based adaptive fuzzy controller for nonlinear dynamic systems
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Robust L2-gain compensative control for direct-adaptive fuzzy-control-system design
IEEE Transactions on Fuzzy Systems
An architecture for adaptive fuzzy control in industrial environments
Computers in Industry
Sliding mode control for uncertain nonlinear systems using RBF neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Controlling force based on radial fuzzy functions in high-speed machining processes
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Adaptive fuzzy sliding mode control of the model of aneurysms of the circle of willis
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given-trajectory. In this paper, two adaptive fuzzy controllers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable in the sense that all signals involved (states, controls, parameters, etc.) must be uniformly bounded. Roughly speaking, the adaptive fuzzy controllers are designed through the following steps: first, construct an initial controller based on linguistic descriptions (in the form of fuzzy IF-THEN rules) about the unknown plant from human experts; then, develop an adaptation law to adjust the parameters of the fuzzy controller on-line. We prove, for both adaptive fuzzy controllers, that: (1) all signals in the closed-loop systems are uniformly bounded; and (2) the tracking errors converge to zero under mild conditions. We provide the specific formulas of the bounds so that controller designers can determine the bounds based on their requirements. Finally, the adaptive fuzzy controllers are used to control the inverted pendulum to track a given trajectory, and the simulation results show that: (1) the adaptive fuzzy controllers can perform successful tracking without using any linguistic information; and (2) after incorporating some linguistic fuzzy rules into the controllers, the adaptation speed becomes faster and the tracking error becomes smaller