Interval Type-2 Fuzzy-Neuro Control of Nonlinear Systems With Proved Overall System Stability

TY - GEN

T1 - Interval Type-2 Fuzzy-Neuro Control of Nonlinear Systems With Proved Overall System Stability

AU - Zhang, Chao

AU - Rossi, Claudio

AU - Kayacan, Erdal

PY - 2017/8/23

Y1 - 2017/8/23

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85030148514&partnerID=8YFLogxK

U2 - 10.1109/FUZZ-IEEE.2017.8015495

DO - 10.1109/FUZZ-IEEE.2017.8015495

M3 - Article in proceedings

SN - 978-1-5090-6034-4

SP - 1

EP - 6

BT - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017

PB - IEEE

T2 - IEEE International Conference on Fuzzy Systems 2017

Y2 - 9 July 2017 through 12 July 2017

ER -