Adaptive control for a class of nonlinear system using general type-2 fuzzy neural networks approximator
Adaptive control for a class of nonlinear system using general type-2 fuzzy neural networks approximator
In this paper, an indirect adaptive controller with general type-2 fuzzy neural networks (GT2FNN) approximator is proposed for a general class of SISO nonlinear systems. General type-2 fuzzy system (GT2FS) can be considered as a collection of numerous interval type-2 fuzzy systems (IT2FS) by α-plane. By using the recently introduced adaptive modulation factor into GT2FNN, the computational complexity and time-consuming are greatly reduced in the type reduction of IT2FS. The KM algorithm can be avoided in the type reduction of GT2FNN. Compared to the conventional GT2FNN, the proposed GT2FNN has obvious advantages in computational complexity and time consumption. The Lyaponov approach proves the stability of the closed-loop system. The simulation results show that the tracking performance of GT2FNN approximator is better than IT2FNN and T1FNN approximator.
- Sichuan University China (People's Republic of)
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!