Authors:
Ieroham Baruch
1
;
Jose Luis Olivares
1
and
Federico Thomas
2
Affiliations:
1
CINVESTAV-IPN, Mexico
;
2
IRI-UPC, Spain
Keyword(s):
Inverse model adaptive neural control, Direct adaptive neural control, Systems identification, Fuzzy-neural hierarchical multi-model, Recurrent trainable neural network, Mechanical system with friction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Fuzzy Control
;
Fuzzy Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Soft Computing
Abstract:
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.