Neural Computing and Applications
Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems
Neural Computing and Applications - Special Issue: Neural networks for control, robotics and diagnostics
Robust adaptive fuzzy control of unknown chaotic systems
Applied Soft Computing
A growing and pruning method for radial basis function networks
IEEE Transactions on Neural Networks
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
FPGA-based adaptive PID control of a DC motor driver via sliding-mode approach
Expert Systems with Applications: An International Journal
Neuroadaptive Combined Lateral and Longitudinal Control of Highway Vehicles Using RBF Networks
IEEE Transactions on Intelligent Transportation Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Applied Soft Computing
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In this paper, an adaptive DRBF neural control (ADNC) system which is composed of a neural controller and a smooth compensator is proposed. The neural controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal controller and neural controller. The DRBF network can self-organizing its network structure. All the controller parameters of the proposed ADNC system are online tuned in the Lyapunov sense, thus the stability analytic shows the system output can exponentially converge to a small neighborhood of the trajectory command. Finally, the proposed ADNC system is applied to a chaotic system and a DC motor. Simulation and experimental results verify that a favorable tracking performance and no chattering phenomena can be achieved by the proposed ADNC system.