Universal approximation using radial-basis-function networks
Neural Computation
Fuzzy adaptive control of multivariable nonlinear systems
Fuzzy Sets and Systems
Adaptive fuzzy control of MIMO nonlinear systems
Fuzzy Sets and Systems
Brief paper: Direct adaptive fuzzy control of nonlinear strict-feedback systems
Automatica (Journal of IFAC)
Fuzzy adaptive observer backstepping control for MIMO nonlinear systems
Fuzzy Sets and Systems
Robust stability of Cohen-Grossberg neural networks via state transmission matrix
IEEE Transactions on Neural Networks
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Adaptive neural network tracking control for a class of non-linear systems
International Journal of Systems Science
IEEE Transactions on Neural Networks
A fuzzy basis function vector-based multivariable adaptivecontroller for nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive Neural Control for a Class of Perturbed Strict-Feedback Nonlinear Time-Delay Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy logic control of discrete-time dynamical systems
Automatica (Journal of IFAC)
Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems
Automatica (Journal of IFAC)
Stable adaptive neurocontrol for nonlinear discrete-time systems
IEEE Transactions on Neural Networks
Adaptive neural control of uncertain MIMO nonlinear systems
IEEE Transactions on Neural Networks
Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
IEEE Transactions on Neural Networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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Based on the backstepping technique, a direct adaptive neural network control algorithm is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. Neural networks are utilized to approximate unknown functions, and a stable adaptive neural backstepping controller is synthesized. It is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the existing results for discrete-time systems, the proposed algorithm needs only less parameters to be adjusted online, therefore, it can reduce online computation burden. A simulation example is employed to illustrate the effectiveness of the proposed algorithm.