Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Fuzzy adaptive control for a class of nonlinear systems
Fuzzy Sets and Systems
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Robust adaptive fuzzy control and its application to ship roll stabilization
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Fuzzy Sets and Systems - Theme: Fuzzy control
An invariance principle for nonlinear hybrid and impulsive dynamical systems
Nonlinear Analysis: Theory, Methods & Applications
Stable indirect fuzzy adaptive control
Fuzzy Sets and Systems - Theme: Modeling and control
Automatica (Journal of IFAC)
Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems
Information Sciences: an International Journal
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
An improved stable adaptive fuzzy control method
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H∞ approaches
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Brief Paper: Design and performance analysis of a direct adaptive controller for nonlinear systems
Automatica (Journal of IFAC)
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The problem of indirect adaptive fuzzy and impulsive control for a class of nonlinear systems is investigated. Based on the approximation capability of fuzzy systems, a novel adaptive fuzzy and impulsive control strategy with supervisory controller is developed. With the help of a supervisory controller, global stability of the resulting closed-loop system is established in the sense that all signals involved are uniformly bounded. Furthermore, the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error. By the generalized Barbalat's lemma, the tracking error between the output of the system and the reference signal is proved to be convergent to zero asymptotically. Simulation results illustrate the effectiveness of the proposed approach.