Nonlinear systems identification and control based on fuzzy-neural multi-model

The paper brings forward a hierarchical fuzzy-neural multi-model and Takagi-Sugeno(T -S) rules with recurrent neural for systems identification, adaptive control of complex nonlinear plants and states estimation. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considered-a two membership functions without overlapping and a three membership functions with overlapping. The simulation shows that good convergent results are obtained.