Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators
Journal of Intelligent and Robotic Systems
Automatica (Journal of IFAC)
H∞ tracking of uncertain SISO nonlinear systems: an observer-based adaptive fuzzy approach
International Journal of Systems Science
International Journal of Intelligent Systems Technologies and Applications
International Journal of Intelligent Systems Technologies and Applications
Adaptive fuzzy controller for non-affine systems with zero dynamics
International Journal of Systems Science
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
IEEE Transactions on Neural Networks
Adaptive neural control for strict-feedback nonlinear systems without backstepping
IEEE Transactions on Neural Networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system
Fuzzy Sets and Systems
Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Sequential support vector machine control of nonlinear systems by state feedback
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
Adaptive NN control of uncertain nonlinear pure-feedback systems
Automatica (Journal of IFAC)
International Journal of Automation and Computing
Intelligent control for long-term ecological systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Information Sciences: an International Journal
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This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions