A local identification method for linear parameter-varying systems based on interpolation of state-space matrices and least-squares approximation

Ferranti, Francesco; Rolain, Yves

doi:10.1016/j.ymssp.2016.05.037

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…Thus, the coefficient matrix Y in Equation ( 31) is also a full column rank, and the solution of the equation must exist. That is to say, in theory, the weighting parameters α i can be arbitrarily chosen (appear in conjugate pairs), and the real feedback gains that meet the pole placement conditions can always be obtained by Equation (31).…”

Section: Receptance-based Active Aeroelastic Controlmentioning

confidence: 99%

“…Only if the information matrix has a relatively small condition number, the problem of observing target structural modes through the sensor outputs is well-conditioned, and Equation (31) does not appear to be ill-conditioned, and the numerical solution exists. Moreover, a larger value of Q s means that the signal energy output by sensors is larger, which is beneficial for improving the sensor noise immunity and implementing active control.…”

Section: Optimal Sensor Placementmentioning

confidence: 99%

“…As shown in Figure 14, the sensor layout obtained at fixed θ = 0 deg causes a large condition number of the information matrix at 21.3 deg; the sensor layout obtained at fixed θ = 60 deg also encounters the same problem at 104.3 deg. Either of these two sensor layouts will lead to ill-conditioned solutions of Equation (31), and the resulting control gains will produce high control effort or invalid control at certain folding angles. As a comparison, the modified method proposed in this paper provides an improved sensor layout, and the condition number of the information matrix has a relatively small value in the entire folding angle range.…”

Section: Implementation Of Thementioning

confidence: 99%

“…Most existing methods cannot handle systems with more than one parameter and require the additional assumption that the number of complex and real poles does not change over the parameter range [28,29]. There are other solutions for coherent representation of the local state-space models, including the balanced realization [30], the least-squares approximation [31], and the SMILE technique proposed by de Caigny et al [32]. These methods more or less have problems such as relying on experience, being difficult to implement, or having harsh conditions.…”

Section: Introductionmentioning

confidence: 99%

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Interpolation-Based Modeling Methodology for Efficient Aeroelastic Control of a Folding Wing

Yue

Zhao

2021

International Journal of Aerospace Engineering

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The aeroelastic model of a folding wing varies with different configurations, so it actually represents a parameter-varying system. Firstly, a new approach based on interpolation of local models is proposed to generate the linear parameter-varying model of a folding wing. This model is capable of predicting the aeroelastic responses during the slow morphing process and is suitable for subsequent control synthesis. The underlying inconsistencies among local linear time-invariant (LTI) models are solved through the modal matching of structural modes and the special treatment of the rational functions in aerodynamic models. Once the local LTI models are represented in a coherent state-space form, the aeroservoelastic (ASE) model at any operating point can be immediately generated by the matrix interpolation technique. Next, based on the present ASE model, the design of a parameterized controller for suppressing the gust-induced vibration is studied. The receptance method is applied to derive fixed point controllers, and the effective independence method is adopted and modified for optimal sensor placement in variable configurations, which can avoid solving ill-conditioned feedback gains. Numerical simulation demonstrates the effectiveness of the proposed interpolation-based modeling approach, and the parameterized controller exhibits a good gust mitigation effect within a wide parameter-varying range. This paper provides an effective and practical solution for modeling and control of the parameterized aeroelastic system.

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Section: Receptance-based Active Aeroelastic Controlmentioning

confidence: 99%

Section: Optimal Sensor Placementmentioning

confidence: 99%

Section: Implementation Of Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpolation-Based Modeling Methodology for Efficient Aeroelastic Control of a Folding Wing

Yue

Zhao

2021

International Journal of Aerospace Engineering

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“…This interpolation is performed by exploiting an interpolating LPV model computed from the estimated set of LTI models (e.g. approaches presented in [10], [11]). Here, a local approach will be exploited.…”

Section: Introductionmentioning

confidence: 99%

On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models

Dias,

Martarelli,

Chiariotti

2024

J. Phys.: Conf. Ser.

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In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input-state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.

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A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit

Zhang

et al. 2017

Applied Energy

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It is essential to establish precise dynamic model for OTB (once-through boiler) power plant unit in order to meet large scale load demand from the power grid. This study proposes to establish such a dynamic mathematical model of ultra-supercritical OTB unit under dry operating condition. More precisely, the dynamic model structure was derived from mass and energy conservation laws as well as thermodynamic principle under some reasonable simplifications and assumptions. Then an IGA (immune genetic algorithm) was improved to identify the parameters, combined with field data. After this, to further enhance model performance, the dynamic mathematical model was extended to be the one with different set of parameters and functions under different monotonous load ranges. Additionally, open-and closed-loop experiments were conducted in order to further validate the developed model. The experimental results shows that the model outputs can approach the actual running data over a wide operating range with appropriate accuracy. More importantly, the dynamic model captures the essential dynamic characteristics of the unit. Thus, the model can be feasible and applicable for simulation analysis and testing control algorithms.

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A local identification method for linear parameter-varying systems based on interpolation of state-space matrices and least-squares approximation

Cited by 14 publications

References 18 publications

Interpolation-Based Modeling Methodology for Efficient Aeroelastic Control of a Folding Wing

Interpolation-Based Modeling Methodology for Efficient Aeroelastic Control of a Folding Wing

On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models

A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit

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