Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A prelude to neural networks: adaptive and learning systems
A prelude to neural networks: adaptive and learning systems
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
A model reference control structure using a fuzzy neural network
Fuzzy Sets and Systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
Stabilization of unknown nonlinear systems using neural networks
Applied Soft Computing
Velocity and position control of a wheeled inverted pendulum by partial feedback linearization
IEEE Transactions on Robotics
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
A supervisory fuzzy neural network control system for tracking periodic inputs
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
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Rule-base structure identification in an adaptive-network-based fuzzy inference system
IEEE Transactions on Fuzzy Systems
Neural-network hybrid control for antilock braking 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
Memory neuron networks for identification and control of dynamical systems
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
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A tracking control of a real inverted pendulum system is implemented in this paper via an adaptive self-constructing fuzzy neural network (ASCFNN) controller. The linear induction motor (LIM) has many excellent performances, such as the silence, high-speed operation and high-starting thrust force, fewer losses and size of motion devices. Therefore, the experiment is implemented by integrating the LIM and an inverted pendulum (IP) system. The ASCFNN controller is composed of an ASCFNN identifier, a computation controller and a robust controller. The ASCFNN identifier is used to estimate parameters of the real IP system and the computational controller is used to sum up the outputs of the ASCFNN identifier. In order to compensate the uncertainties of the system parameters and achieve robust stability of the considered system, the robust controller is adopted. Furthermore, the structure and parameter learning are designed in the ASCFNN identifier to achieve favorable approximation performance. The Mahalanobis distance (M-distance) method in the structure learning is also employed to determine if the fuzzy rules are generated/eliminated or not. Concurrently, the adaptive laws are derived based on the sense of Lyapunov so that the stability of the system can be guaranteed. Finally, the simulation and the actual experiment are implemented to verify the effectiveness of the proposed ASCFNN controller.