Sliding mode control of a discrete system
Systems & Control Letters
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 hybrid neuro-fuzzy PID controller
Fuzzy Sets and Systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Adaptive controller with fuzzy rules emulated structure and its applications
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
A neural network-based approximation method for discrete-time nonlinear servomechanism problem
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This article introduces the adaptive controller for a class of nonlinear discrete-time systems based on the sliding shuttering condition and the self adjustable network called Multi-Input Fuzzy Rules Emulated Network (MIFREN). By using only the online learning phase, MIFREN's functional is the nonlinear discrete-tine function approximation and the disturbance estimation together. The proposed theorem is introduced for the designing procedure of all controller's parameters and MIFREN's adaptation gain. Simulation results demonstrate the justification of the theorem for the tracking performance and the unknown disturbance rejection.