Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

Zhou, Hai Bo; Ying, Hao; Duan, Jianguo

doi:10.1007/s11771-011-0760-0

Cited by 19 publications

(3 citation statements)

References 13 publications

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“…However, the external disturbances are not considered in this research. In [18,19], the interval fuzzy type-II controller is introduced for nonlinear uncertain systems. It is proved that the fuzzy type-II controller handles uncertainties and external disturbances better than the fuzzy type-I controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Type-II Controller for Wheeled Mobile Robot with Disturbances and Wheelslips

Pham

2021

Journal of Robotics

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This paper proposed adaptive fuzzy type-II controllers for the wheeled mobile robot (WMR) systems under conditions of wheel slips and disturbances. The system includes two control loops: outer loop for position tracking and the inner loop for velocity tracking. In each loop, the controller has two parts: the feedback which keeps the system stable and the adaptive type-II fuzzy part which is used to compensate the unknown components that act on the system. The stability of each loop as well as the overall system is proven mathematically based on the Lyapunov theory. Finally, the simulation is setup to verify the effectiveness of the presented algorithm. The simulation results show that, in comparison with the corresponding fuzzy type-I controller, the performance of the adaptive fuzzy type-II controller is better, i.e., the position error is smaller and the velocity is almost smooth under the conditions that the reference trajectory is changed, and the system is affected by wheel slips and external disturbances.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Type-II Controller for Wheeled Mobile Robot with Disturbances and Wheelslips

Pham

2021

Journal of Robotics

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“…This shows that IT2FLSs have better performance than type-1 fuzzy logic systems (T1FLSs) when confronted with various uncertainties such as dynamic uncertainties, rule uncertainties, external disturbances and noises. See Zhou et al, 36 Ghaemi et al, 37 Wu and Tan 38 and Lin et al 39 . Available information that is used to construct the rules in an FLS can be uncertain.…”

Section: Introductionmentioning

confidence: 99%

A new and robust control strategy for a class of nonlinear power systems: Adaptive general type-II fuzzy

Khooban

Niknam

2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a robust direct adaptive general type-2 fuzzy logic controller is introduced for a class of nonlinear power systems. The proposed controller uses the advantages of general type-2 fuzzy logic system in handling dynamic uncertainties to approximate unknown nonlinear actions. Implementing general type-2 fuzzy system is computationally costly; however, by using a recently introduced a-plane representation, general type-2 fuzzy logic system can be seen as a composition of several interval type-2 fuzzy logic systems with a corresponding level of a for each. Linguistic rules are directly incorporated into the controller. In addition, an H ' compensator is corrupted to attenuate external disturbance and fuzzy approximation error. General type-2 fuzzy adaptation laws are also derived using Lyapunov approach. It is worth noting that mathematical analysis proves the stability of the closed-loop system. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by direct adaptive type-1 fuzzy logic controller, a direct adaptive interval type-2 fuzzy logic controller and adaptive proportional-integral-derivative, which are the latest researches in the problem in hand. The proposed controller is applied to a chaotic power system as a case study. Simulation reveals the effectiveness of the proposed controller in presence of dynamic uncertainties and external disturbances.

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“…Type‐2 fuzzy sets (T2FSs) as an extension of type‐1 fuzzy sets (T1FSs) were first introduced by Zadeh [10] in 1975. Since then, type‐2 fuzzy logic systems especially interval type‐2 fuzzy logic systems (IT2FLSs) because of their calculation simplicity have been successfully applied to engineering areas, which show IT2FLSs have better performance than type‐1 fuzzy logic systems (T1FLSs) when confronted with various uncertainties like dynamic uncertainties, rule uncertainties, external disturbances and noises, for example, see [11–14]. Hagras and Wagner [15] showed that type‐2 fuzzy logic systems, in contrast to T1FLSs are able to handle higher level of linguistic and numerical uncertainties when applying to real world applications.…”

Section: Introductionmentioning

confidence: 99%

Direct adaptive general type‐2 fuzzy control for a class of uncertain non‐linear systems

Ghaemi

Sani

Khooban³

2014

IET Science, Measurement & Technology

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Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

Cited by 19 publications

References 13 publications

Adaptive Fuzzy Type-II Controller for Wheeled Mobile Robot with Disturbances and Wheelslips

Adaptive Fuzzy Type-II Controller for Wheeled Mobile Robot with Disturbances and Wheelslips

A new and robust control strategy for a class of nonlinear power systems: Adaptive general type-II fuzzy

Direct adaptive general type‐2 fuzzy control for a class of uncertain non‐linear systems

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