Adaptive Backstepping Hybrid Fuzzy Sliding Mode Control for Uncertain Fractional-Order Nonlinear Systems Based on Finite-Time Scheme

Song, Shuai; Zhang, Baoyong; Xia, Jianwei; Zhang, Zhengqiang

doi:10.1109/tsmc.2018.2877042

Cited by 124 publications

(52 citation statements)

References 51 publications

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“…2) in which the corresponding control system is described by a nonlinear fractional-order model. Among these 9 cases, control system analysis and design in the case of NLF process and NLF controller, as the most general case, has received more attention than the other ones (some samples of the methods proposed in this case for control of nonlinear fractional-order systems are nonlinear fractional PI control [107], predictive control [108], adaptive sliding mode control [109], adaptive backstepping control [110], adaptive neurofuzzy control [111], and adaptive iterative learning control [112]). Nevertheless, the other cases yielding a nonlinear fractional-order control system have been also considered in literature, e.g.…”

Section: Nonlinear Fractional-order Control Systemsmentioning

confidence: 99%

Nonlinear Fractional-Order Circuits and Systems: Motivation, A Brief Overview, and Some Future Directions

Tavazoei

Tavakoli-Kakhki

Bizzarri

2020

IEEE Open J. Circuits Syst.

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In recent years, fractional-order differential operators, and the dynamic models constructed based on these generalized operators have been widely considered in design and practical implementation of electrical circuits and systems. Simultaneously, facing with fractional-order dynamics and the nonlinear ones in electrical circuits and systems enforces us to use more advanced tools (in comparison to those commonly used in design and analysis of linear fractional-order/nonlinear integer-order circuits and systems) for their analysis, design, and implementation. Discussing on such a motivation, this tutorial paper aims to provide an overview on the recent achievements in proposing effective tools for analysis and design of nonlinear fractional-order circuits and systems. Moreover, some open problems, which can specify future directions for continuing research works on the aforementioned subject, are discussed.

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Section: Nonlinear Fractional-order Control Systemsmentioning

confidence: 99%

Nonlinear Fractional-Order Circuits and Systems: Motivation, A Brief Overview, and Some Future Directions

Tavazoei

Tavakoli-Kakhki

Bizzarri

2020

IEEE Open J. Circuits Syst.

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“…In [29]- [31], nonsingular terminal SMC strategies combined with the Fractional-order theory were designed for cable-driven manipulators. Fuzzy sliding mode theory combined with Fractional-order theory was proposed for uncertain Fractional-order nonlinear systems in [32]. In [33], [34], dynamic surface control strategies combined with the Fractional-order theory were designed for Fractional-order nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Super Twisting Nonlinear Fractional Order PID Sliding Mode Control of Permanent Magnet Synchronous Motor Speed Regulation System Based on Extended State Observer

et al. 2020

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In this article, a novel adaptive super-twisting nonlinear Fractional-order PID sliding mode control (ASTNLFOPIDSMC) strategy using extended state observer (ESO) for the speed operation of permanent magnet synchronous motor (PMSM) is proposed. Firstly, this paper proposes a novel nonlinear Fractional-order PID (NLFOPID) sliding surface with nonlinear proportion term, nonlinear integral term and nonlinear differential term. Secondly, the novel NLFOPID switching manifold and an adaptive supertwisting reaching law (ASTRL) are applied to obtain excellent control performance in the sliding mode phase and the reaching phase, respectively. The novel ASTNLFOPIDSMC strategy is constructed by the ASTRL and the NLFOPID sliding surface. Due to the utilization of NLFOPID switching manifold, the characteristics of fast convergence, good robustness and small steady state error can be ensured in the sliding mode phase. Due to the utilization of ASTRL, the chattering phenomenon can be weakened, and the characteristics of high accuracy and strong robustness can be obtained in the reaching phase. Further, an ESO is designed to achieve dynamic feedback compensation for external disturbance. Furthermore, Lyapunov stability theorem and Fractional calculus are used to prove the stability of the system. Finally, comparison results under different controllers demonstrate that the proposed control strategy not only achieves good stability and dynamic properties, but also is robust to external disturbance. INDEX TERMS Adaptive super-twisting nonlinear Fractional-order PID sliding mode control (ASTNL-FOPIDSMC) strategy, extended state observer (ESO), permanent magnet synchronous motor (PMSM), nonlinear Fractional-order PID (NLFOPID) sliding surface, adaptive super-twisting reaching law (ASTRL).

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“…Due to their universal approximation properties, fuzzy logic systems (FLSs) or neural networks (NNs) were utilized to identify the unknown functions in the nonlinear systems. A large number of adaptive control schemes based on fuzzy or neural approximation have been reported in other works . Nevertheless, few efforts have been invested in the precision of the fuzzy or NNs approximation, as well as the estimation errors of FLSs or NNs.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of adaptive control schemes based on fuzzy or neural approximation have been reported in other works. [9][10][11][12][13][14] Nevertheless, few efforts have been invested in the precision of the fuzzy or NNs approximation, as well as the estimation errors of FLSs or NNs. To improve the aforementioned methods, an adaptive control method with a composite identification model designed for nonlinear systems was proposed by Hojati and Gazor.…”

Section: Introductionmentioning

confidence: 99%

A neural composite dynamic surface control for pure‐feedback systems with unknown control gain signs and full state constraints

Liu

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper investigates a composite neural dynamic surface control (DSC) method for a class of pure‐feedback nonlinear systems in the case of unknown control gain signs and full‐state constraints. Neural networks are utilized to approximate the compound unknown functions, and the approximation errors of neural networks are applied in the design of updated adaptation laws. Comparing the proposed composite approximation method with the conventional ones, a faster and better approximation performance result can be obtained. Combining the composite neural networks approximation with the DSC technique, an improved composite neural adaptive control approach is designed for the considered nonlinear system. Then, together with the Lyapunov stability theory, all the variables of the closed‐loop system are semiglobal uniformly ultimately bounded. The infringements of full state constraints can be avoided in the case of unknown control gain signs as well as unknown disturbances. Finally, two simulation examples show the effectiveness and feasibility of the proposed results.

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Adaptive Backstepping Hybrid Fuzzy Sliding Mode Control for Uncertain Fractional-Order Nonlinear Systems Based on Finite-Time Scheme

Cited by 124 publications

References 51 publications

Nonlinear Fractional-Order Circuits and Systems: Motivation, A Brief Overview, and Some Future Directions

Nonlinear Fractional-Order Circuits and Systems: Motivation, A Brief Overview, and Some Future Directions

An Adaptive Super Twisting Nonlinear Fractional Order PID Sliding Mode Control of Permanent Magnet Synchronous Motor Speed Regulation System Based on Extended State Observer

A neural composite dynamic surface control for pure‐feedback systems with unknown control gain signs and full state constraints

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