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
Adaptive Tracking Control of Nonlinear Systems Using Neural Networks
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Stable adaptive control with recurrent neural networks for square MIMO non-linear systems
Engineering Applications of Artificial Intelligence
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
B-Spline Output Feedback Control for Nonlinear Systems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Lyapunov theory-based multilayered neural network
IEEE Transactions on Circuits and Systems II: Express Briefs
Indirect sliding mode neural-network control for holonomic constrained robot manipulators
International Journal of Intelligent Systems Technologies and Applications
Transport control of underactuated cranes
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Dynamic structure neural network for stable adaptive control of nonlinear systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Engineering Applications of Artificial Intelligence
Intelligent control for long-term ecological systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme