Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems
Information Sciences: an International Journal
Fuzzy adaptive output feedback control for MIMO nonlinear systems
Fuzzy Sets and Systems
A neural-fuzzy system for congestion control in ATM networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tracking a maneuvering target using neural fuzzy network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stable multi-input multi-output adaptive fuzzy/neural control
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems
IEEE Transactions on Fuzzy Systems
Fuzzy adaptive sliding-mode control for MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Mixed Feedforward/Feedback Based Adaptive Fuzzy Control for a Class of MIMO Nonlinear Systems
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems
IEEE Transactions on Fuzzy Systems
Automatica (Journal of IFAC)
Adaptive neural control of uncertain MIMO nonlinear systems
IEEE Transactions on Neural Networks
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
IEEE Transactions on Neural Networks
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process
Information Sciences: an International Journal
Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks
Information Sciences: an International Journal
Computational intelligence approach to PID controller design using the universal model
Information Sciences: an International Journal
Information Sciences: an International Journal
MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators
Information Sciences: an International Journal
Information Sciences: an International Journal
A fuzzy control system with application to production planning problems
Information Sciences: an International Journal
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Grey-prediction self-organizing fuzzy controller for robotic motion control
Information Sciences: an International Journal
Adaptive control for nonlinear MIMO time-delay systems based on fuzzy approximation
Information Sciences: an International Journal
Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network
Engineering Applications of Artificial Intelligence
Enhanced adaptive grey-prediction self-organizing fuzzy sliding-mode controller for robotic systems
Information Sciences: an International Journal
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Applied Soft Computing
Fuzzy modeling approach to predictions of chemical oxygen demand in activated sludge processes
Information Sciences: an International Journal
Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems
Integrated Computer-Aided Engineering
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This paper proposes a novel dynamic structure neural fuzzy network (DSNFN) to address the adaptive tracking problems of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control scheme uses a four-layer neural fuzzy network (NFN) to estimate system uncertainties online. The main feature of this DSNFN is that it can either increase or decrease the number of fuzzy rules over time based on tracking errors. Projection-type adaptation laws for the network parameters are derived from the Lyapunov synthesis approach to ensure network convergence and stable control. A hybrid control scheme that combines the sliding-mode control and the adaptive bound estimation control with different weights improves system performance by suppressing the influence of external disturbances and approximation errors. As the employment of the DSNFN, high-quality tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. Simulations performed on a two-link robot manipulator demonstrate the effectiveness of the proposed control scheme.