State-Space Recurrent Fuzzy Neural Networks for Nonlinear System Identification
Neural Processing Letters
H∞ tracking of uncertain SISO nonlinear systems: an observer-based adaptive fuzzy approach
International Journal of Systems Science
A unified approach for design of indirect adaptive output-feedback fuzzy controller
International Journal of Intelligent Systems Technologies and Applications
System identification using hierarchical fuzzy neural networks with stable learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC
Neural Processing Letters
GA-Based Adaptive Fuzzy-Neural Control for a Class of MIMO Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Recurrent Fuzzy CMAC for Nonlinear System Modeling
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Robust and Adaptive Fuzzy Feedback Linearization Regulator Design
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Intelligent hybrid control strategy for trajectory tracking of robot manipulators
Journal of Control Science and Engineering
Adaptive fuzzy tracking control of nonlinear systems
WSEAS Transactions on Systems and Control
Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems
Engineering Applications of Artificial Intelligence
Adaptive fuzzy controller for non-affine systems with zero dynamics
International Journal of Systems Science
Observer-based fuzzy adaptive control for strict-feedback nonlinear systems
Fuzzy Sets and Systems
Fuzzy adaptive observer backstepping control for MIMO nonlinear systems
Fuzzy Sets and Systems
Time-optimal control of T-S fuzzy models via lie algebra
IEEE Transactions on Fuzzy Systems
Fuzzy adaptive output feedback control for MIMO nonlinear systems
Fuzzy Sets and Systems
Mean-based fuzzy identifier and control of uncertain nonlinear systems
Fuzzy Sets and Systems
Analysis of time-optimal problem in T-S fuzzy model via Lie algebra
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Control and Intelligent Systems
Information Sciences: an International Journal
Information Sciences: an International Journal
A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Sliding mode control for uncertain nonlinear systems using RBF neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Adaptive fuzzy output-feedback controller for SISO affine nonlinear systems without state observer
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Interval type 2 hierarchical FNN with the H-infinity condition for MIMO non-affine systems
Applied Soft Computing
Estimation of indicated torque for performance monitoring in a diesel engine
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Design of a unified adaptive fuzzy observer for uncertain nonlinear systems
Information Sciences: an International Journal
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In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance