A finite-time H-infinite adaptive fault-tolerant controller considering time delay for flutter suppression of airfoil flutter

Abstract

To suppress airfoil flutter, a lot of control methods have been proposed, such as classical control methods and optimal control methods. However, these methods did not consider the influence of actuator faults and control delay. This paper proposes a new finite-time H∞ adaptive fault-tolerant flutter controller by radial basis function neural network technology and adaptive fault-tolerant control method, taking into account actuator faults, control delay, modeling uncertainties, and external disturbances. The theoretic section of this paper is about airfoil flutter dynamic modeling and adaptive fault-tolerant controller design. Lyapunov function and linear matrix inequality are employed to prove the stability of the proposed control method of this paper. The numeral simulation section further proves the effectiveness and robustness of the proposed control algorithm of this paper.

Aeroelastic analysis and flutter control of wings and panels: A review

“…It can be found that, compared with the uncontrolled aeroelastic system, a small feedback time‐delay can enhance the flutter boundary, while a large value of the feedback time‐delay will reduce the aeroelastic stability of the airfoil. Gao et al 153 designed a finite‐time adaptive fault‐tolerant dither controller based on neural network technology and adaptive fault‐tolerant control method to suppress the flutter of a 2D airfoil. The effectiveness and robustness of the control method were proved through numerical simulations.…”

Simple adaptive V-stack piezoelectric based airfoil flutter suppression system

“…The ASP property implies that exists a constant output feedback gain that makes the plant strictly passive (Rusnak and Barkana, 2009) (Barkana, 2016) and that the system transfer function fulfill the Almost Strictly Positive Realness ASPR condition. For a SISO transfer function the ASPR condition implies that its relative degree is 1, the leading coefficient of its numerator is positive and all its zeros are in the left half complex plane (Matsuki et al, 2018) (Iwai et al, 2006). However, almost all real systems do not comply with the ASPR condition; thus, to apply the SAC control algorithm, an augmented plant is defined by adding a Parallel Feed-forward Compensator PFC (Barkana, 2007).…”

“…Jia et al (2019, 2021) proposed a data-driven optimal controller which eliminates the effects of non-modeled dynamics and uncertainties. Ming-Zhou et al (2020) proposed a finite-time H∞ adaptive fault-tolerant control that takes into account control delay, uncertainties, and disturbances.…”