A hierarchical fuzzy-neural multi-model: an application for a mechanical system with friccion identification and control

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of two control schemes, which are: a trajectory tracking control by an inverse FNHMM and a direct adaptive control, using the states issued by the identification FNHMM. The proposed control methods are applied for a mechanical plant with friction system control, where the obtained comparative results show that the control using FNHMM outperforms the fuzzy and the neural single control.