A fuzzy-neural multi-model for nonlinear systems identification and control
The paper proposed to apply it hierarchical fuzzy-neural multi-model and Takagi-Sugeno (T-S) rules with recurrent neural procedural consequent part for systems identification. states estimation and adaptive control of complex nonlinear plants. 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. In both cases a good convergent results are obtained. (C) 2008 Elsevier B.V. All rights reserved.
recurrent neural networks fuzzy-neural hierarchical multi-model systems identitication indirect adaptive control direct adaptive control dc-motor control