Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A detailed study of adaptive control of chaotic systems with unknown parameters
Dynamics and Control
Fuzzy modeling and adaptive control of uncertain chaotic systems
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences—Informatics and Computer Science: An International Journal
Brief Paper: Dynamical analysis and control of microcantilevers
Automatica (Journal of IFAC)
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
Adaptive dynamic RBF neural controller design for a class of nonlinear systems
Applied Soft Computing
Chaos synchronization of nonlinear gyros using self-learning PID control approach
Applied Soft Computing
Robust H∞ fuzzy control of dithered chaotic systems
Neurocomputing
State observer based dynamic fuzzy logic system for a class of SISO nonlinear systems
International Journal of Automation and Computing
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This paper presents a robust adaptive fuzzy control algorithm for controlling unknown chaotic systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal controller, based on sliding-mode control. The robust controller is designed to compensate for the difference between the fuzzy controller and the ideal controller. The parameters of the fuzzy system, as well as uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the stability of the controlled system. Numerical simulations show the effectiveness of the proposed approach.