A Fuzzy-Neural Hierarchical Multi-model for Systems Identification and Direct Adaptive Control
نویسندگان
چکیده
A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for local identification and local control of complex nonlinear plants. The RTNN model is incorporated in Hierarchical Fuzzy-Neural Multi-Model (HFNMM) architecture, combining the fuzzy model flexibility with the learning abilities of the RTNNs. A direct feedback/feedforward HFNMM control scheme using the states issued by the identification FNHMM is proposed. The proposed control scheme is applied for 1-DOF mechanical plant with friction, and the obtained results show that the control using HFNMM outperforms the fuzzy and the single RTNN one.
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