Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor
Engineering Applications of Artificial Intelligence
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems
Expert Systems with Applications: An International Journal
Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm
Expert Systems with Applications: An International Journal
A fuzzy neural network with fuzzy impact grades
Neurocomputing
IEEE Transactions on Neural Networks
ARFNNs with SVR for prediction of chaotic time series with outliers
Expert Systems with Applications: An International Journal
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
Adaptive fuzzy wavelet neural controller design for chaos synchronization
Expert Systems with Applications: An International Journal
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
Adaptive dynamic RBF neural controller design for a class of nonlinear systems
Applied Soft Computing
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model
Information Sciences: an International Journal
| Hi-index | 0.01 |
This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) via sliding-mode approach for a class of nonlinear systems. The proposed SAFNC system is comprised of a computation controller and a supervisory controller. The computation controller including a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller. The SOFNN identifier is used to online estimate the controlled system dynamics with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure learning phase possesses the ability of online generation and elimination of fuzzy rules to achieve optimal neural structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The supervisory controller is used to achieve the L2-norm bound tracking performance with a desired attenuation level. Moreover, all the parameter learning algorithms are derived based on Lyapunov function candidate, thus the system stability can be guaranteed. Finally, simulation results show that the SAFNC can achieve favorable tracking performances.