Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A course in fuzzy systems and control
A course in fuzzy systems and control
Variable structure control design for uncertain dynamic systems with sector nonlinearities
Automatica (Journal of IFAC)
Journal of Optimization Theory and Applications
Adaptive Control
Adaptive Control of Systems with Actuator and Sensor Nonlinearities
Adaptive Control of Systems with Actuator and Sensor Nonlinearities
Adaptive control for a class of second-order nonlinear systems with unknown input nonlinearities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A supervisory fuzzy neural network control system for tracking periodic inputs
IEEE Transactions on Fuzzy Systems
The adaptive control of nonlinear systems using the Sugeno-type of fuzzy logic
IEEE Transactions on Fuzzy Systems
An optimal tracking neuro-controller for nonlinear dynamic systems
IEEE Transactions on Neural Networks
Direct adaptive control of wind energy conversion systems using Gaussian networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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This paper presents an adaptive single neural controller for a class of uncertain nonlinear systems subject to a nonlinear input. A new type of neuron called auto-tuning neuron with three adjustable parameters will be introduced to construct a single neural controller. From the concept of the sliding mode control, a simple adaptation law, minimizing the value of a designed sliding condition based on a modified MIT rule, is developed for online updating these parameters in the auto-tuning neuron, even if the nonlinear plant considered is with the uncertainty, external noisy perturbation, and nonlinear input. Lastly, a controlled well-known Duffing Holmes chaotic system is illustrated to show the effectiveness of the proposed neural controller.