Information Sciences—Informatics and Computer Science: An International Journal
Sugeno based robust adaptive fuzzy sliding mode controller for SISO nonlinear systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
International Journal of Systems, Control and Communications
A novel adaptive NN control for a class of strict-feedback nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
ACC'09 Proceedings of the 2009 conference on American Control Conference
A simple adaptive fuzzy control for a class of strict-feedback SISO systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
IEEE Transactions on Fuzzy Systems
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Adaptive neural network dynamic surface control for perturbed nonlinear time-delay systems
International Journal of Automation and Computing
NN based adaptive dynamic surface control for fully actuated AUV
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Application-Oriented adaptive neural networks design for ship's linear-tracking control
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
DSC approach to robust adaptive NN tracking control for a class of SISO systems
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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An adaptive fuzzy robust tracking control (AFRTC) algorithm is proposed for a class of nonlinear systems with the uncertain system function and uncertain gain function, which are all the unstructured (or nonrepeatable) state-dependent unknown nonlinear functions arising from modeling errors and external disturbances. The Takagi-Sugeno type fuzzy logic systems are used to approximate unknown uncertain functions and the AFRTC algorithm is designed by use of the input-to-state stability approach and small gain theorem. The algorithm is highlighted by three advantages: 1) the uniform ultimate boundedness of the closed-loop adaptive systems in the presence of nonrepeatable uncertainties can be guaranteed; 2) the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed; and 3) the adaptive mechanism with minimal learning parameterizations can be obtained. The performance and limitations of the proposed method are discussed. The uses of the AFRTC for the tracking control design of a pole-balancing robot system and a ship autopilot system to maintain the ship on a predetermined heading are demonstrated through two numerical examples. Simulation results show the effectiveness of the control scheme.