Classical and Fuzzy Sliding Mode Control for a Nonlinear Aeroelastic System with Unsteady Aerodynamic Model

Rafiee, Zahra; Lagha, Mohand; Smain, Dilmi

doi:10.12785/ijcds/090608

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…In the following, this two step design procedure is applied for the stabilization of the wing. The sliding surface is chosen with the typical form [15]…”

Section: Sliding Mode Controlmentioning

confidence: 99%

Stochastic robustness analysis of wing-aileron flutter suppression systems

Vindigni,

Mantegna,

Esposito

et al. 2024

J. Phys.: Conf. Ser.

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In this work a stochastic robustness analysis approach is used to compare the performances of two non-linear flutter suppression systems designed for stabilization of a wing-aileron lifting structure. A reduced order modelling approach based on an alternative aeroelastic beam finite element is presented and used for the flutter suppression systems design, which are driven by a simple adaptive controller and a sliding mode controller, respectively. A heuristic algorithm is used to determine the simple adaptive controller invariant parameters at the design point while a simple parametric study is performed to tune the sliding mode controller. The performances of each closed loop system are evaluated taking into account mass, stiffnesses, and aerodynamics uncertainties of the aeroelastic plant also considering the presence of discrete gust disturbances.

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“…In the following, this two step design procedure is applied for the stabilization of the wing. The sliding surface is chosen with the typical form [15]…”

Section: Sliding Mode Controlmentioning

confidence: 99%

Stochastic robustness analysis of wing-aileron flutter suppression systems

Vindigni,

Mantegna,

Esposito

et al. 2024

J. Phys.: Conf. Ser.

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“…The sliding model was first proposed in 1990 and applied to aerospace control at the beginning of the 21st century [18]. In the following 20 years, scholars have improved SMC strategies with different compensation methods [19][20][21]. In recent years, deep learning has brought epic changes in various fields.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Engineering Frequency Domain Analysis and Vibration Suppression of Flexible Aircraft Based on Active Disturbance Rejection Controller

Liu

Tian

2022

Sensors

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The crash of an aircraft with an almost vertical attitude in Wuzhou, Guangxi, China, on March 21, 2022, has caused a robust discussion in the civil aviation community. We propose an active disturbance rejection controller (ADRC) for suppressing aeroelastic vibrations of a flexible aircraft at the simulation level. The ADRC has a relatively simple structure and it has been proved in several fields to provide better control than the classical proportional-integral-derivative (PID) control theory and is easier to translate from theory to practice compared with other modern control theories. In this paper, the vibration model of the flexible aircraft was built, based on the first elastic vibration mode of the aircraft. In addition, the principle of ADRC is explained in detail, a second-order ADRC was designed to control the vibration model, and the system’s closed-loop frequency domain characteristics, tracking effect and sensitivity were comprehensively analyzed. The estimation error of the extended state observer (ESO) and the anti-disturbance effect were analyzed, while the robustness of the closed-loop system was verified using the Monte Carlo method, which was used for the first time in this field. Simulation results showed that the ADRC suppressed aircraft elastic vibration better than PID controllers and that the closed-loop system was robust in the face of dynamic parameters.

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