Chaos synchronization of brushes direct current motors for electric vehicle: Adaptive fuzzy immersion and invariance approach

Mohammadzadeh, Ardashir; Ahmadian, Ali; Elkamel, Ali; Alhameli, Falah

doi:10.1177/0142331220953667

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…For this purpose, different control methods have been developed to eliminate the chaos phenomena. Adaptive control (Mohammadzadeh et al, 2021), sliding mode control (SMC) (Ghasemi et al, 2018; Yousefpour et al, 2020), robust control (Pujol-Vazquez et al, 2020), fuzzy logic control (Rajagopal et al, 2020), and neural network control (Zhang et al, 2019) are proposed to control the chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Control and synchronization of chaotic spur gear system using adaptive non-singular fast terminal sliding mode controller

Khaniki

Kho

Shoorehdeli

2022

Transactions of the Institute of Measurement and Control

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This study reports a novel adaptive non-singular fast terminal sliding mode controller for the tracking control and synchronization of a chaotic spur gear system. The proposed novel control law attenuates the chattering phenomena of the conventional sliding mode controller. In addition, a non-singular fast terminal sliding mode surface is employed to remove the singularity problem, increase the convergence rate, and guarantee finite-time convergence. An extreme learning machine (ELM) neural network is utilized to estimate the unknown dynamics of the spur gear system and the reaching law coefficients; hence, this control scheme is a combination of the direct and indirect adaptive control. The adaptation rules of the ELM are derived based on the Lyapunov stability theorem to ensure closed-looped stability. Finally, some different numerical simulations are considered to check the validity and efficiency of the proposed control strategy compared with other control methods.

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Section: Introductionmentioning

confidence: 99%

Control and synchronization of chaotic spur gear system using adaptive non-singular fast terminal sliding mode controller

Khaniki

Kho

Shoorehdeli

2022

Transactions of the Institute of Measurement and Control

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“…In addition, the TS model is a convex combination of linear models that expedites stability analysis, complex system modeling, controller design, and other related concepts within the context of linear matrix inequalities (LMIs) (Ghalehnoie et al, 2020; Lendek et al, 2011; Sun et al, 2019). Fuzzy logic–based control of EV subsystems has come out as one of the most strong and prolific aspects of inquiry in recent years (Mohammadzadeh et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Constrained integral fuzzy sliding mode control for an electric vehicle: An LMI-based approach

Amiri

Moosavi

Forouzanfar

et al. 2022

Transactions of the Institute of Measurement and Control

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This study investigates the issue of stability analysis and controller synthesis for an electric vehicle based on the Takagi–Sugeno fuzzy control approach. The proposed adaptive fuzzy integral sliding mode controller ensures that the electric vehicle system performs satisfactorily in the presence of uncertainties and exogenous disturbances, comprising low computational load, small control input amplitude, and reduced energy consumption. For efficient performance of the electric vehicle, the battery voltage (control input) and the vehicle’s speed (system output) are considered restricted. Simulations of an electric vehicle powered by a brushed direct current motor are utilized to evaluate the efficacy of the proposed controller. Ultimately, the design procedure validates the robust performance and fast stabilization of the electric vehicle speed in the presence of exogenous disturbances and parametric uncertainties.

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A type-2 fuzzy control for active/reactive power control and energy storage management

Moghadam

Mohammadzadeh

Tavoosi

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper presents a new type-2 fuzzy logic control (T2FLC) for active/reactive power (AP/RP) regulation and energy storage management of an independent microgrid (MG). The case-study MG includes sporadic renewable sources that lead to significant frequency and voltage fluctuations in the network. The rules of suggested T2FLCs are tuned such that a fractional-order sliding condition can be satisfied. Also a new approach using square-root-cubature Kalman filter is designed to optimize the membership parameters of T2FLCs. Beside AP/RP power regulation, an electrical storage system (battery energy storage system (BESS)) is managed to produce the rated power, rather than a conventional generator. It makes the device frequency separate from the synchronous generator’s mechanical inertia. Nevertheless, a BESS has a power limit – it uses a synchronous generator to sustain the BESS charge state at a certain value. Sustainable energies achieve a voltage-damping impact by supplementing an AP/RP droop strategy procedure that considerably reduces voltage oscillation created by variation of its own output power. In various disturbed scenarios the good performance of the suggested controller and energy management system is shown.

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Chaos synchronization of brushes direct current motors for electric vehicle: Adaptive fuzzy immersion and invariance approach

Cited by 3 publications

References 40 publications

Control and synchronization of chaotic spur gear system using adaptive non-singular fast terminal sliding mode controller

Control and synchronization of chaotic spur gear system using adaptive non-singular fast terminal sliding mode controller

Constrained integral fuzzy sliding mode control for an electric vehicle: An LMI-based approach

A type-2 fuzzy control for active/reactive power control and energy storage management

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