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 C: Applications and Reviews
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
Nonlinear control structures based on embedded neural system models
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A neural network-based approximation method for discrete-time nonlinear servomechanism problem
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An approximate internal model-based neural control for unknown nonlinear discrete processes
IEEE Transactions on Neural Networks
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This article introduces an adaptive controller for a class of unknown nonlinear discrete-time systems based on multi-input fuzzy rules emulated network (MIFREN). By the estimation of any nonlinear systems from MIFREN, this network is assigned to identify the unknown system under control. The proposed control law is introduced by the result of nonlinear system identification based on MIFREM and the defined sliding condition. Without the need of any off-line learning phase, all control parameters including the learning rate for MIFREN are selected to guarantee the bonded signals such as the model error, tuned weight vector, the tracking error and the sliding signal via the defined Lyapunov functions and proposed theorems. The performance of the proposed control algorithm is demonstrated and the main theorem is validated by computer simulation results.