Fractional controller based on a robust <i>PI</i><sup>α</sup> observer for uncertain fractional systems

Trejo-Zúñiga, Iván; Delfin-Prieto, Sergio M.; Martínez-Guerra, Rafael

doi:10.1080/00207721.2019.1574043

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…Researchers in [13][14][15] have proposed to add different supplementary controllers aiming to eliminate the appearing time lag. In this paper, the proposed contribution is the L 1 adaptive fractional control based on fractional calculus [16][17][18][19]. en, the new idea is to carefully choose the filter.…”

Section: Introductionmentioning

confidence: 99%

$L_{1}$ Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

Maalej

Khlif

Mhiri

et al. 2021

Mathematical Problems in Engineering

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Recently, an adaptive control approach has been proposed. This approach, named L 1 adaptive control, involves the insertion of a low-pass filter at the input of the Model Reference Adaptive Control (MRAC). This controller has been designed to overcome several limitations of classical adaptive controllers such as (i) the initialization of estimated parameters, (ii) the stability problems with high adaptation gains, and (iii) the appropriate parameter excitation. In this paper, a new design of the filter is presented, used for L 1 adaptive control, for which the desired performances are guaranteed (appropriate values of the control during start-up, a high filtering of noises, a reduced time lag, and a reduced energy consumption). Parameters of the new proposed filter have been optimised by genetic algorithms. The proposed L 1 adaptive fractional control is applied to a polyarticulated robotic system. Simulation results show the efficiency of the proposed control approach with respect to the classical L 1 adaptive control in the nominal case and in the presence of a multiplicative noise.

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Section: Introductionmentioning

confidence: 99%

$L_{1}$ Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

Maalej

Khlif

Mhiri

et al. 2021

Mathematical Problems in Engineering

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show abstract

State estimation in mechanical systems of fractional-order based on a family of proportional $${\varvec{\rho }}$$-integral observers

Martínez-Fuentes,

Muñoz-Vázquez,

Fernández-Anaya

et al. 2023

Nonlinear Dyn

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Stability analysis of fractional-order linear system with PID controller in the output feedback structure subject to input saturation

Fiuzy

Shamaghdari

2021

Int. J. Dynam. Control

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Fractional controller based on a robust PI^α observer for uncertain fractional systems

Cited by 6 publications

References 35 publications

$L_{1}$ Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

$L_{1}$ Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

State estimation in mechanical systems of fractional-order based on a family of proportional $${\varvec{\rho }}$$-integral observers

Stability analysis of fractional-order linear system with PID controller in the output feedback structure subject to input saturation

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Fractional controller based on a robust PIα observer for uncertain fractional systems

Cited by 6 publications

References 35 publications

L 1 Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

L 1 Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

State estimation in mechanical systems of fractional-order based on a family of proportional $${\varvec{\rho }}$$-integral observers

Stability analysis of fractional-order linear system with PID controller in the output feedback structure subject to input saturation

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Fractional controller based on a robust PI^α observer for uncertain fractional systems

$L_{1}$ Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

$L_{1}$ Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems