Neural Processing Letters
On Neural Network Switched Stabilization of SISO Switched Nonlinear Systems with Actuator Saturation
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Robotic Agent Control Based on Adaptive Intelligent Algorithm in Ubiquitous Networks
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Neural Networks Sliding Mode Control for a Class of Switched Nonlinear Systems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
PMLSM controller design based on self-constructing feedback fuzzy neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
RBF based induction motor control with a good nonlinearity compensation
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
H∞ neural networks control for uncertain nonlinear switched impulsive systems
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
H-Infinity control for switched nonlinear systems based on RBF neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a σ-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given