ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS
Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in modelling of highly nonlinear components in the system. A laboratory set-up...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/7120/1/05898130.pdf http://irep.iium.edu.my/7120/ http://dx.doi.org/10.1109/UKRICIS.2010.5898130 |
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| Summary: | Artificial intelligence techniques, such as neural
networks and fuzzy logic have shown promising results for
modelling of nonlinear systems whilst traditional approaches are
rather insufficient due to difficulty in modelling of highly
nonlinear components in the system. A laboratory set-up that
resembles the behaviour of a helicopter, namely twin rotor multiinput multi-output system (TRMS) is used as an experimental rig
in this research. An adaptive neuro-fuzzy inference system
(ANFIS) tuned by particle swarm optimization (PSO) algorithm
is developed in search for non-parametric model for the TRMS.
The antecedent parameters of the ANFIS are optimized by a PSO
algorithm and the consequent parameters are updated using
recursive least squares (RLS). The results show that the proposed
technique has better convergence and better performance in
modeling of a nonlinear process. The identified model is justified
and validated in both time domain and frequency domain |
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