Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
A course in fuzzy systems and control
A course in fuzzy systems and control
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
Advanced Fuzzy Systems Design and Applications
Advanced Fuzzy Systems Design and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy logic controller for an ABS braking system
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
Nonlinear adaptive control using networks of piecewise linear approximators
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Neuro-sliding mode control with its applications to seesaw systems
IEEE Transactions on Neural Networks
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A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed new learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations and in the experimental studies, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.