Abstract
In this paper, we put forward an interval type-2 fuzzy neural network (IT2FNN) to deal with control issues of nonlinear systems with uncertainties. The fuzzy rules of the IT2FNN use interval type-2 triangular fuzzy sets to account for antecedent parts and adopt crisp numbers for the corresponding consequents. To effectively cope with uncertainties in the systems, a sliding-mode-control theory-based approach with new parameter learning rules is proposed to update the IT2FNN. The overall stability of the proposed methodology is also proved by using appropriate Lyapunov functions. Finally, the proposed method is applied to control the angular position of an inverted pendulum system. Simulation results indicate that, compared to a conventional proportional-derivative controller, the IT2FNN with the proposed learning rules can eliminate the uncertainties in performance and efficiently track the angle trajectory as desired.
| Original language | English |
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| Title of host publication | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 23 Aug 2017 |
| Pages | 1-6 |
| Article number | 8015495 |
| ISBN (Print) | 978-1-5090-6034-4 |
| ISBN (Electronic) | 9781509060344 |
| DOIs | |
| Publication status | Published - 23 Aug 2017 |
| Externally published | Yes |
| Event | IEEE International Conference on Fuzzy Systems 2017 - Royal-Continental Hotel Via Partenope, Italy Duration: 9 Jul 2017 → 12 Jul 2017 |
Conference
| Conference | IEEE International Conference on Fuzzy Systems 2017 |
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| Location | Royal-Continental Hotel Via Partenope |
| Country/Territory | Italy |
| Period | 09/07/2017 → 12/07/2017 |