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
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
Robust fuzzy neural network control for linear ceramic motor drive via backstepping design technique
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Neural-network approximation of piecewise continuous functions: application to friction compensation
IEEE Transactions on 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
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
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
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In this paper, an adaptive growing-and-pruning neural network control (AGPNNC) system is developed for a linear piezoelectric ceramic motor. The AGPNNC system is composed of a neural controller and a robust controller. The neural controller uses a self-constructing neural network (SCNN) to mimic an ideal computation controller, and the robust controller is designed to achieve L"2 tracking performance with desired attenuation level. If the approximation performance of the SCNN is inadequate, the SCNN can create new hidden neurons to increase learning ability. If the hidden neuron of the SCNN is insignificant, it should be removed to reduce computation loading; otherwise, if the hidden neuron of the SCNN is significant, it should be retained. Moreover, the adaptive laws of controller parameters are derived in the sense of Lyapunov function and Barbalat's lemma; so the system stability can be guaranteed. Finally, experimental results show that a perfect tracking response can be achieved using the self-constructing network mechanism and the on-line parameter-learning algorithm.