Nonlinear discrete-time controller with unknown systems identification based on fuzzy rules emulated network ?
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.
nonlinear discrete-time adaptive control neuro-fuzzy