Journal of Intelligent and Robotic Systems
Design of a Robust Adaptive Neural Tracking Controller
Journal of Intelligent and Robotic Systems
Fuzzy-Logic-Based Adaptive Control for a Class of Nonlinear Discrete-Time System
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
A real-time neuro-adaptive controller with guaranteed stability
Applied Soft Computing
Fuzzy controllers for a class of discrete-time nonlinear systems
Artificial Intelligence Review
Sampled-data adaptive NN tracking control of uncertain nonlinear systems
IEEE Transactions on Neural Networks
Discrete-Time adaptive controller design for robotic manipulators via neuro-fuzzy dynamic inversion
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Brief Robot discrete adaptive control based on dynamic inversion using dynamical neural networks
Automatica (Journal of IFAC)
Adaptive H8 Fuzzy Control for a Class of Uncertain Discrete-Time Nonlinear Systems
International Journal of Artificial Life Research
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For a class of MIMO sampled-data nonlinear systems with unknown dynamic nonlinearities, a stable neural-network (NN)-based adaptive control approach which is an integration of an NN approach and the adaptive implementation of the variable structure control with a sector, is developed. The sampled-data nonlinear system is assumed to be controllable and its state vector is available for measurement. The variable structure control with a sector serves two purposes. One is to force the system state to be within the state region in which the NN's are used when the system goes out of neural control; and the other is to provide an additional control until the system tracking error metric is controlled inside the sector within the network approximation region. The proof of a complete stability and a tracking error convergence is given and the setting of the sector and the NN parameters is discussed. It is demonstrated that the asymptotic error of the system can be made dependent only on inherent network approximation errors and the frequency range of unmodeled dynamics. Simulation studies of a two-link manipulator show the effectiveness of the proposed control approach