Combing Instrumental Variable and Variance Matching for Aircraft Flutter Model Parameters Identification

Jianwang, Hong; Ramírez-Mendoza, Ricardo A.; Lozoya-Santos, Jorge de J.

doi:10.1155/2019/4296091

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The above detailed descriptions on identification or estimation problem for aircraft flutter model parameters is from our previously published paper (Jianwang et al , 2019). In recent years, we have some contributions about the aircraft flutter model parameters identification.…”

Section: Introductionmentioning

confidence: 99%

“…To give a more clear understanding about our existing contributions, we introduce our published results as follows next. When the observed input signal-output data are corrupted by the observed noises in the aircraft flutter stochastic model, the instrumental variable method and variance matching are combined to be one instrumental variable variance method, used to identify the unknown parameters in one constructed transfer function form (Jianwang et al , 2019). This new proposed instrumental variable variance method achieves good estimation accuracy only on the condition of the independent and identically distributed random noises.…”

Section: Introductionmentioning

confidence: 99%

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Closed-loop identification for aircraft flutter model parameters

Wang

2022

AEAT

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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 This aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model. Originality/value To the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Closed-loop identification for aircraft flutter model parameters

Wang

2022

AEAT

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Subspace Data-Driven Control for Linear Parameter Varying Systems

Wang

Zhang

Ramirez‐Mendoza

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This control paves the way for investigating the nonlinear system based on the results regarding the linear system that are already known. Only the data matrix is utilized to represent the output prediction value in the future various time instants, while the input-output observation data matrix is used to identify Markov parameters in the form of state space forms. The cost function in data-driven control is then adjusted using the output prediction value. The optimal control input value of this quadratic cost function is solved using a parallel distribution technique, and the algorithm's iterative convergence is thoroughly examined. Finally, the DC motor, whose mass distribution factor is considered to be one linear parameter varying system, is controlled using the suggested subspace data driven control approach.

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Combing Instrumental Variable and Variance Matching for Aircraft Flutter Model Parameters Identification

Cited by 2 publications

References 13 publications

Closed-loop identification for aircraft flutter model parameters

Closed-loop identification for aircraft flutter model parameters

Subspace Data-Driven Control for Linear Parameter Varying Systems

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