Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Robust adaptive control
A robust adaptive nonlinear control design
Automatica (Journal of IFAC)
Fuzzy adaptive control for a class of nonlinear systems
Fuzzy Sets and Systems
Stable fuzzy adaptive control for a class of nonlinear systems
Fuzzy Sets and Systems
Fuzzy adaptive control of multivariable nonlinear systems
Fuzzy Sets and Systems
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Fuzzy Control
Stable indirect fuzzy adaptive control
Fuzzy Sets and Systems - Theme: Modeling and control
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Stable multi-input multi-output adaptive fuzzy/neural control
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 hybrid adaptive fuzzy control for a class of nonlinear MIMO systems
IEEE Transactions on Fuzzy Systems
Brief Robust tracking control for nonlinear MIMO systems via fuzzy approaches
Automatica (Journal of IFAC)
An iterated fuzzy extended Kalman filter for nonlinear systems
International Journal of Systems Science
Stable adaptive fuzzy control for MIMO nonlinear systems
Computers & Mathematics with Applications
International Journal of Fuzzy System Applications
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This article presents a direct adaptive fuzzy control scheme for a class of uncertain continuous-time multi-input multi-output nonlinear (MIMO) dynamic systems. Within this scheme, fuzzy systems are employed to approximate an unknown ideal controller that can achieve control objectives. The adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller. The stability analysis of the closed-loop system is performed using a Lyapunov approach. In particular, it is shown that the tracking errors are bounded and converge to a neighborhood of the origin. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance.