Stable adaptive systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Feedback linearization using neural networks
Automatica (Journal of IFAC)
Feedback linearization using CMAC neural networks
Automatica (Journal of IFAC)
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
An improved stable adaptive fuzzy control method
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy robust tracking controller design via small gain approach and its application
IEEE Transactions on Fuzzy Systems
Survey Constructive nonlinear control: a historical perspective
Automatica (Journal of IFAC)
Adaptive neural network control of tendon-driven mechanisms with elastic tendons
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Dynamic structure neural networks for stable adaptive control of nonlinear systems
IEEE Transactions on Neural Networks
Nonlinear adaptive trajectory tracking using dynamic neural networks
IEEE Transactions on Neural Networks
Using radial basis functions to approximate a function and its error bounds
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural networks controller for time-varying systems
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Neural network based controller for constrained multivariable systems
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Expert Systems with Applications: An International Journal
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In this paper, we propose a novel adaptive H"~ tracking controller design for a class of nonlinear systems with uncertain system and gain function, which are unstructured (or non-repeatable) and state-dependent unknown nonlinear functions. Both the separation technique of continuous function and radial-basis-function (RBF) neural network are incorporated to approximate the uncertain system function. Systematic design procedure is developed for the synthesis of adaptive neural network tracking control with H"~ performance. The resulting closed-loop system is proven to be semi-globally uniformly ultimately bounded and the effect of the external disturbances on the tracking error can be attenuated to any prescribed level. In addition, the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed and the adaptive mechanism with only one learning parameterizations can be achieved. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Finally, simulation results show the effectiveness of the control scheme.