Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay

Li, Hongyi; Wang, Lijie; Du, Haiping; Boulkroune, Abdesselem

doi:10.1109/tfuzz.2016.2567457

Cited by 298 publications

(142 citation statements)

References 37 publications

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“…A fuzzy logic system consists of four parts: the knowledge base, the fuzzifier, the fuzzy inference engine working on the fuzzy rules, and the defuzzifier [34][35][36][37][38][39][40]48]. Usually, a fuzzy logic system is modeled bŷ(…”

Section: Description Of a Fuzzy Systemmentioning

confidence: 99%

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Adaptive Fuzzy Synchronization of Fractional-Order Chaotic Neural Networks with Backlash-Like Hysteresis

Liu

2018

Advances in Mathematical Physics

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An adaptive fuzzy synchronization controller is designed for a class of fractional-order neural networks (FONNs) subject to backlash-like hysteresis input. Fuzzy logic systems are used to approximate the system uncertainties as well as the unknown terms of the backlash-like hysteresis. An adaptive fuzzy controller, which can guarantee the synchronization errors tend to an arbitrary small region, is given. The stability of the closed-loop system is rigorously analyzed based on fractional Lyapunov stability criterion. Fractional adaptation laws are established to update the fuzzy parameters. Finally, some simulation examples are provided to indicate the effectiveness and the robust of the proposed control method.

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Section: Description Of a Fuzzy Systemmentioning

confidence: 99%

“…Up to now, fuzzy control methods have been studied extensively [34][35][36][37][38][39][40][41][42]. Specially, this approach has been particularly used to synchronize or control integer-order neural networks (IONNs) [43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Synchronization of Fractional-Order Chaotic Neural Networks with Backlash-Like Hysteresis

Liu

2018

Advances in Mathematical Physics

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“…(24) Using inequalities (23), (24) and the fractional direct Lyapunov method in Lemma 1, the sufficient condition can be written as…”

Section: V T D X T Px T X T P D X T T T X Pxmentioning

confidence: 99%

“…In general, tracking control design is more general and more difficult than the stabilization control design. Various systems including strict-feedback systems [23], networked control systems [15], neural networks [12], stochastic Lagrangian systems [5], etc., the tracking controller design methods have 368 Copyright ⓒ 2017 SERSC been proposed. For tracking control problem with H∞ performance, reference [22] has discussed the fuzzy control design method for nonlinear systems with a guaranteed H∞ model reference tracking performance, and a novel neural-network-based robust H∞ control strategy is proposed for the trajectory following problem of robot manipulators in [25] and so on.…”

Section: Introductionmentioning

confidence: 99%

H∞ Tracking Control for Nonlinear Fractional-Order Systems Via Output Feedback Design

Song¹,

Song²,

Liu³

2017

IJCA

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“…The tracking control has be seen in various industrial system [12,13], the objective of this control is to design a tracking controller and to converge the output to the desired reference model without a tracking error. Compared with stability control design and stabilization problems, the tracking control problem is more difficult especially for nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

The Fuzzy Tracking Control of Output vector of Double Fed Induction Generator DFIG via T–S Fuzzy Model

Abdelmalki¹,

Ouaaline²

2018

IJIES

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Abstract:The purpose of this paper is to ensure a tracking control of output state especially the active and reactive powers of doubly fed induction generator (DFIG) by using the approach of Parallel Distributed Compensation (PDC) of the fuzzy control type Takagie-Sugeno (T-S) which determines the control laws containing fuzzy tracking and return state. This tracking has been built to converge the output vectors of the DFIG to a desired state. In this work, the quadratic function of lyaponov and a linear matrix inequality (LMI) are used to obtain the gains of the tracking control and the controller.The simulations results of law control based on the tracking and controller gains allow the active and reactive powers obtained must follow the reference proposed.

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Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay

Cited by 298 publications

References 37 publications

Adaptive Fuzzy Synchronization of Fractional-Order Chaotic Neural Networks with Backlash-Like Hysteresis

Adaptive Fuzzy Synchronization of Fractional-Order Chaotic Neural Networks with Backlash-Like Hysteresis

H∞ Tracking Control for Nonlinear Fractional-Order Systems Via Output Feedback Design

The Fuzzy Tracking Control of Output vector of Double Fed Induction Generator DFIG via T–S Fuzzy Model

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