Nonlinear Discrete-Time Adaptive Controller Based on Fuzzy Rules Emulated Network and Its Estimated Gradient

The adaptive controller for a class of nonlinear discrete-time systems based on Multi-Input Fuzzy Rules Emulated Network (MIFREN) is introduced in this article. MIFREN is assigned to identify the unknown plant under control, then a novel control law is introduced based the previously identified plant with another MIFREN. All control parameters, including the learning rates are selected to guarantee bounded close-loop signals, via Lyapunov stability criteria. The performance of the proposed control algorithm is demonstrated by computer simulation results.