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Set Membership Strategy for Closed Loop aircraft flutter Controller Design

IEEE ACCESS 2024年 12卷 52315-52324页

作者： Liu, Yunhong Wang, Jianhong Xinyu Boxun Automobile Co Ltd Xinyu 100027 Jiangxi Peoples R China Sch Engn & Sci Tecnol Monterrey Monterrey 64849 Mexico

We consider the problem of set membership identification and control for closed loop aircraft flutter platform within the practical case of bounded noise. Several mathematical derivations are presented for closed loop aircraft flutter identification and control through our mentioned set membership strategy. First, a more practical closed loop aircraft flutter platform is constructed with the external bounded noise, extending the classical while noise and statistical noise. Then set membership strategy is proposed to achieve the dual goals of flutter identification and flutter control. More specifically, set membership identification is applied to identify the unknown parameters, thus relating with the flutter model frequency and damping coefficient simultaneously. Meanwhile, set membership control is proposed to construct one linearly parameterized aircraft flutter controller, while replacing external noise by its bounded value, and controller parameters are chosen as their corresponding mean values. Finally, numerical example is exemplified to show the applicability of the theoretical results. Generally, here our contributions are two folds. One is set membership identification and the other is set membership control, while combining closed loop structure and bounded noise for closed loop aircraft flutter platform.

关键词： Closed loop aircraft flutter set membership identification set membership control

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Optimal input signal design for aircraft flutter model parameters identification

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aircraft ENGINEERING AND AEROSPACE TECHNOLOGY 2021年第10期93卷 1559-1572页

作者： Wang Jianhong JiangXi Univ Sci & Technol Sch Elect & Automat Ganzhou Peoples R China

Purpose The purpose of this paper considers optimal input signal design for flutter model parameters identification, as input signal is the first step during the whole identification process. According to the constructed flutter stochastic model with observed noises, separable least squares identification and set membership identification are proposed to identify those unknown model parameters for statistical noise and unknown but bounded noise, respectively. The common trace operation with respect to the asymptotic variance matrix is minimized to solve the power spectral for the optimal input signal in the framework of statistical noise. Moreover, for the unknown bout bounded noise, the radius of information, corresponding to the established parameter uncertainty interval, is minimized to give the optimal input signal. Design/methodology/approach First, model identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter statistical model with statistical noise, separable least squares identification is proposed to identify the unknown model parameters, then the optimal input signal is designed to satisfy one given performance function. Third, for aircraft flutter model with unknown but bounded noise, set membership identification is proposed to solve the parameter set for each unknown model parameter. Then, the optimal input signal is designed by applying the idea of the radius of information with unknown but bounded noise. Findings This aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Then identification strategy and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise and unknown but bounded noise, respectively. Originality/value To the best

关键词： aircraft flutter Bounded noise Model parameter Optimal input signal Statistical noise

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Direct data-driven strategy for closed-loop aircraft flutter test

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aircraft ENGINEERING AND AEROSPACE TECHNOLOGY 2023年第5期95卷 749-756页

作者： Wang, Jianhong Ramirez-Mendoza, Ricardo A. Jiangxi Univ Sci & Technol Coll Elect Engn & Automat Ganzhou Peoples R China

PurposeThis new paper aims to extend the authors' previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data-driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data-driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors' own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data-driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data-driven strategy. Generally, during our new information age, direct data-driven strategy is widely applied around our living life. Design/methodology/approachFirst, consider one more complex closed loop stochastic aircraft flutter model, whose input-output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data-driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data-driven control is proposed to design the feedback controller, which suppresses the flutter response actively. FindingsA novel direct data-driven strategy is proposed to achieve the dual missions, i.e. iden

关键词： aircraft flutter Closed loop Direct-data driven identification Direct data-driven control

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Closed-loop identification for aircraft flutter model parameters

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aircraft ENGINEERING AND AEROSPACE TECHNOLOGY 2022年第7期94卷 1117-1127页

作者： Wang Jianhong Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou Peoples R China

Purpose The purpose of this paper is to extend the authors' previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today's practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input-output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller. Design/methodology/approach First, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller. Findings Th

关键词： aircraft flutter Model parameter Identification algorithm Optimal input signal Statistical noise Optimal feedback controller

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Instrumental variable covariance method for the aircraft flutter model parameter identification

Instrumental variable covariance method for the aircraft flu...

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24th Chinese Control and Decision Conference (CCDC)

作者： Wang, Jianhong Zhu Yong-hong Jingdezhen Ceram Inst Sch Mech & Elect Engn Jingdezhen 333403 Peoples R China

ISBN: (纸本)9781457720727

When the observed input-output datas are corrupted with observed noises in the aircraft flutter statistic model, we should obtain the more exact aircraft flutter model parameters to predict the flutter boundary accuracy and assure flight safety of plane. So we combine the instrumental variable identification method in system identification theory and covariance matching method in modern spectrum theory to get a new strategy-instrumental variable covariance method. In aircraft flutter's statistic model, we introduce some instrumental variable to develop a covariance function. And we propose a new criterion function which is composed as a difference between the theory value and actual estimation value of the covariance function. Now the new criterion function based on the covariance function can be used to identify some unknown parameter vector in parameterization frequency domain response function, furthermore we give the procedure in detail to solve the new criterion function and correspond to the partial derivatives expression. Finally we apply this new identification method to identify the transfer function in current loop of flight simulator and aircraft flutter model parameter identification. The simulation with real flight test data shows the efficiency of the algorithm.

关键词： aircraft flutter model parameter instrumental variable covariance method

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Instrumental variable covariance method and asymptotic analysis for the aircraft flutter model parameter identification

Instrumental variable covariance method and asymptotic analy...

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31st Chinese Control Conference

作者： Yao Jie Wang Jianghong Jingdezhen Ceram Inst Sch Mech & Elect Engn Jingdezhen 333403 Peoples R China

ISBN: (纸本)9789881563811

When the observed input-output datas are corrupted with observed noises in the aircraft flutter statistic model, we should obtain the more exact aircraft flutter model parameters to predict the flutter boundary accuracy and assure flight safety of plane. So we combine the instrumental variable identification method in system identification theory and covariance matching method in modern spectrum theory to get a new strategy-instrumental variable covariance method. In aircraft flutter's statistic model, we introduce some instrumental variable to develop a covariance function. And we propose a new criterion function which is composed as a difference between the theory value and actual estimation value of the covariance function. Now the new criterion function based on the covariance function can be used to identify some unknown parameter vector in parameterization frequency domain response function, furthermore we give the procedure in detail to solve the new criterion function and correspond to the partial derivatives expression. By virtue of the accuracy analysis theory in system identification setup, we derive the asymptotic covariance matrix expression which is obtained from this paper's instrumental variable covariance method. Then we can use this asymptotic covariance matrix expression to judge the effectiveness of this new identification method and design the external input excite signal. Finally we apply this new identification method to identify the transfer function in current loop of flight simulator and aircraft flutter model parameter identification. The simulation with real flight test data shows the efficiency of the algorithm.

关键词： aircraft flutter model parameter instrumental variable covariance method asymptotic analysis

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Data-Driven Approach to the Development of an Aeroelastic flutter Precursor

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AIAA JOURNAL 2025年第1期63卷 174-183页

作者： Meivar, Boaz Karpel, Moti Technion Israel Inst Technol IL-32000 H_efa Israel

This paper presents a novel algorithm for aeroelastic flutter early detection. Two new features for flutter onset detection are presented. flutter early warning is accomplished using only measured signals, with essentially no prior knowledge needed about the aircraft or the flutter mechanism involved. The algorithm consists of three stages: 1) extraction of regularity features, 2) calibration by addition of white noise to nominal measurements, and 3) thresholding. Four types of datasets were used: a) synthetic data, b) simulated data generated using aeroelastic response simulations to stochastic gusts, c) measured data from a wind tunnel experiment, and d) flight test data including actual flutter onsets. The algorithm was shown to be able to flag an impending flutter event before critical onset occurs. (The Python code for paper is available at https://***/bmeivar/flutter.)

关键词： Flight Testing Wind Tunnels Aeroelastic Modeling Probability Density Functions Power Spectral Density Gaussian Mixture Models flutter Analysis aircraft flutter Data Science

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flutter Calculations Using the Unsteady Source and Doublet Panel Method

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JOURNAL OF aircraft 2025年第1期62卷 117-131页

作者： Dimitriadis, Grigorios Kilimtzidis, Spyridon Kostopoulos, Vassilis Laraspata, Vito Soria, Leonardo Univ Liege Aerosp & Mech Engn Dept B-4000 Liege Belgium Univ Patras Dept Mech Engn & Aeronaut Rion 26504 Greece Polytech Univ Bari Dept Mech Math & Management I-70126 Bari Italy

The source and doublet panel method (SDPM) developed by Morino in the 1970s can model unsteady compressible ideal flow around wings and bodies. In this work, the SDPM is adapted to the calculation of aeroelastic solutions for wings. A second-order nonlinear version of Bernoulli's equation is transformed to the frequency domain and written in terms of the generalized mode shapes and displacements. It is shown that the pressure component at the oscillating frequency is a linear function of the generalized displacements, velocities, and accelerations and can therefore be used to formulate a linear flutter problem. The proposed approach has several advantages over the usual doublet lattice method (DLM) approach: the exact geometry is modeled (including thickness, camber, and twist effects), the motion of the surface can be represented using all six degrees of freedom, the pressure calculation is of higher order, and the aerodynamic mass, damping, and stiffness terms are calculated explicitly. The complete procedure is validated using experimental data from the weakened AGARD 445.6 wing, a NACA 0012 rectangular wing with pitch and plunge degrees of freedom, and an experimental model of a T-tail, yielding flutter predictions that lie closer to the experimental observations than those obtained from the DLM, particularly in the case of the T-tail.

关键词： Aerodynamic Stability Unsteady Aerodynamic Modeling Aeroelasticity aircraft flutter Potential Flow Panel Methods Finite Element Analysis

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IDENTIFICATION OF SYSTEM, OBSERVER, AND CONTROLLER FROM CLOSED-LOOP EXPERIMENTAL-DATA

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 1994年第1期17卷 91-96页

作者： JUANG, JN PHAN, M NASA Langley Research Center Hampton Virginia 23681

This paper considers the identification problem of a system operating in a closed loop with an existing feedback controller. The closed-loop system is excited by a known excitation signal, and the resulting time histories of the closed-loop system response and the feedback signal are measured. From the time history data, the algorithm computes the Markov parameters of a closed loop observer, from which the Markov parameters of the individual open-loop plant, observer, and controller are recovered. A state-space model of the open-loop plant and the gain matrices for the controller and the observer are then realized. The results of the paper are demonstrated by an example using wind tunnel aircraft flutter test data.

关键词： State Space Representation aircraft flutter Wind Tunnel Tests Feedback Control System Observer Kalman Filter Identification Eigensystem Realization Algorithm Multiple Input Multiple Output Singular Value Decomposition Hubble Space Telescope NASA Langley Research Center

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Robust Generalized Predictive Control with Uncertainty Quantification

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2012年第3期35卷 930-937页

作者： Lew, Jiann-Shiun Juang, Jer-Nan Tennessee State Univ Ctr Excellence Informat Syst Nashville TN 37209 USA Natl Cheng Kung Univ Tainan 70101 Taiwan

This paper presents a robust predictive control method with uncertainty quantification for problems, such as online aircraft flutter suppression. The proposed approach begins by identifying the multistep output prediction matrices under various test conditions from online time-domain input/output data. Singular value decomposition is then used to characterize and quantify the parameter uncertainties of the prediction matrices. The predicted response error due to parameter uncertainties is integrated into the predictive control design process for robustness. The proposed method provides an innovative and efficient way to incorporate the quantified uncertainty in predictive control design with a linear process. The proposed approach is demonstrated by application to the Benchmark Active Controls Technology wind-tunnel model.

关键词： Predictive Control Method Benchmark Active Control Technology aircraft flutter Dynamic Pressure Singular Value Decomposition Vibration Suppression Wind Tunnel Models Feedback Control Flight Vehicle Observer Kalman Filter Identification

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