Command-filtered compound FAT learning control of fractional-order nonlinear systems with input delay and external disturbances

Keighobadi, Javad; Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Machado, J. A. Tenreiro

doi:10.1007/s11071-022-07203-1

Cited by 10 publications

(3 citation statements)

References 64 publications

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“…The authors in [28] propose an observer-based feedback control scheme for a class of fractional-order systems with input delay, in which the Smith predictor is employed to transform the system into a fractional-order system without input delay. For the uncertain FONSs with input delay and external disturbances considered in [29], a fractional-order adaptive learning controller is developed by using function approximation technique, and a augmented controller is designed to compensate the input delay effects. In [30], the authors propose an adaptive control scheme by using fractional-order command-filter for uncertain FONSs with subjected to input delay, in which a fractional integral is introduced to deal with the input delay.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Adaptive NN Backstepping Dynamic Surface Control for Input-Delay Fractional-Order Nonlinear Systems

Xing

Wang

2023

IEEE Access

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This article focuses on a finite-time adaptive dynamic surface control (DSC) approach for a kind of nonstrict fractional-order nonlinear systems (FONSs) with input delay. An auxiliary compensation function is presented by using the integral function of the input signal to handle input delay problem. To overcome the problem of inherent computational complexity, a fractional-order filter is applied to approach the virtual controller and its fractional-order derivative in each step of the backstepping procedure. By using backstepping technology and neural network (NN), the finite-time adaptive DSC approach is developed, and finite-time stability criteria based on fractional-order Lyapunov method are introduced to prove the finite-time convergence of the tracking error into a small region around the origin. The effectiveness of the presented control scheme is demonstrated by two examples. INDEX TERMSNonlinear systems; Neural network; Finite-time; FONSs; Input delay.

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Section: Introductionmentioning

confidence: 99%

Finite-Time Adaptive NN Backstepping Dynamic Surface Control for Input-Delay Fractional-Order Nonlinear Systems

Xing

Wang

2023

IEEE Access

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“…Inspired by these methods, the RBFNN is employed to solve unknown nonlinear terms. More further, regarding the delayed processing, the authors (Keighobadi, Pahnehkolaei, et al, 2022a) introduce a fractional-order delay compensation system to handle the input delay. In Hua et al, 2022, the authors solve the input delay of a coupling PMSM by constructing LKFs, and the method can solve larger time delays compared to the result (Keighobadi, Pahnehkolaei, et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

“…More further, regarding the delayed processing, the authors (Keighobadi, Pahnehkolaei, et al, 2022a) introduce a fractional-order delay compensation system to handle the input delay. In Hua et al, 2022, the authors solve the input delay of a coupling PMSM by constructing LKFs, and the method can solve larger time delays compared to the result (Keighobadi, Pahnehkolaei, et al, 2022a). However, they ignore the effect of time delays, which may lead to poor performance and system instability (Lu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Neural adaptive finite-time dynamic surface control for the PMSM system with time delays and asymmetric time-varying output constraint

Zhang

et al. 2022

Journal of Vibration and Control

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This paper presents a neural adaptive finite-time dynamic surface control for the permanent magnet synchronous motor system with time delays and asymmetric time-varying output constraint. The core challenge is how to address the time delays and asymmetric output constraint when designing a finite-time control scheme. Given this, a proper Lyapunov–Krasovskii functional is introduced to address time delays, and a nonlinear transformation function is considered to convert the output-constrained problem into an unconstrained one. Then, a neural adaptive finite-time dynamic surface control approach is devised in the finite-time backstepping framework, which applies neural networks to estimate the unknown nonlinear functions and introduces first-order filters to solve the “explosion of complexity” problems. Furthermore, it is demonstrated that all the signals of the resulting system are finite-time stable and the tracking error converges to a neighborhood of origin in finite time without violating the output constraint. Finally, the simulation results show that the integration of squared error results, the integral of time and absolute error results as well as the integration of absolute value error results of the proposed scheme is smaller than the tested scheme by 0.3458 [Formula: see text], 22.2977 [Formula: see text], and 2.2513 [Formula: see text], respectively, when the time delays are considered. It further elucidates the availability and superiority of the developed method.

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A graphical method-based Kharitonov theorem for robust stability analysis of incommensurate fractional-order uncertain systems

Ebrahimi,

Alaviyan Shahri,

Alfi

2024

Comp. Appl. Math.

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Command-filtered compound FAT learning control of fractional-order nonlinear systems with input delay and external disturbances

Cited by 10 publications

References 64 publications

Finite-Time Adaptive NN Backstepping Dynamic Surface Control for Input-Delay Fractional-Order Nonlinear Systems

Finite-Time Adaptive NN Backstepping Dynamic Surface Control for Input-Delay Fractional-Order Nonlinear Systems

Neural adaptive finite-time dynamic surface control for the PMSM system with time delays and asymmetric time-varying output constraint

A graphical method-based Kharitonov theorem for robust stability analysis of incommensurate fractional-order uncertain systems

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