A parameter estimation perspective of continuous time model reference adaptive control
Automatica (Journal of IFAC)
Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
A self-tuning fuzzy controller
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A new fuzzy logic learning control scheme for repetitive trajectory tracking problems
Fuzzy Sets and Systems - Theme: Fuzzy control
Variable neural networks for adaptive control of nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H∞ approaches
IEEE Transactions on Fuzzy Systems
Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators
IEEE Transactions on Fuzzy Systems
Fuzzy adaptive sliding-mode control for MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Effects of moving the center's in an RBF network
IEEE Transactions on Neural Networks
An iterated fuzzy extended Kalman filter for nonlinear systems
International Journal of Systems Science
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This article proposes a novel fuzzy system, referred to as a dynamic structure fuzzy system, to address tracking control problems for unknown nonlinear dynamical systems. The fuzzy system is employed to reconstruct the unknown nonlinearities of dynamic systems. In the dynamic structure fuzzy system, the number of fuzzy rules can be either increased or decreased over time based on the required approximation accuracy. The advantage of the dynamic structure fuzzy system is that a suitable-sized fuzzy system can be found to avoid overfitting or underfitting data sets. By using Gaussian radial basis function (GRBF) as a membership function, adaptation laws are presented for tuning all parameters of the parameterized fuzzy system, including the output weights, the widths and the centers of the GRBF's. Global boundedness of the overall control scheme is guaranteed in the sense of Lyapunov. The tracking error converges to the required precision through the adaptive control scheme derived by the Lyapunov synthesis approach. Simulations performed on an underwater vehicle system demonstrate the effectiveness of our scheme.