SummaryIn this paper, a general approach to address modeling of aeroelastic systems, with the final goal to apply μ analysis, is discussed. The chosen test bed is the typical section with unsteady aerodynamic loads, which enables basic modeling features to be captured and so extend the gained knowledge to practical problems treated with modern techniques. The aerodynamic operator has a nonrational dependence on the Laplace variable s, and hence, 2 formulations for the problem are available: frequency domain or state-space (adopting rational approximations). The study attempts to draw a parallel between the 2 consequent linear fractional transformation modeling processes, emphasizing critical differences and their effect on the predictions obtained with μ analysis. A peculiarity of this twofold formulation is that aerodynamic uncertainties are inherently treated differently and therefore the families of plants originated by the possible linear fractional transformation definitions are investigated. One of the main results of the paper is to propose a unified framework to address the robust modeling task, which enables the advantages of both the approaches to be retained.On the analysis side, the application of μ analysis to the different models is shown, emphasizing its capability to gain insight into the problem.

Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these methods lead to ill-conditioned datadriven model structures. In this work, we present a maximum likelihood framework to obtain an optimal data-driven model, the signal matrix model, in the presence of output noise. Data compression and noise level estimation schemes are also proposed to apply the algorithm efficiently to large datasets and unknown noise level scenarios. Two approaches in system identification and receding horizon control are developed based on the derived optimal estimator. The first one identifies a finite impulse response model. This approach improves the least-squares estimator with less restrictive assumptions. The second one applies the signal matrix model as the predictor in predictive control. The control performance is shown to be better than existing data-driven predictive control algorithms, especially under high noise levels. Both approaches demonstrate that the derived estimator provides a promising framework to apply data-driven algorithms to noisy data.

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This paper formulates an input design approach for impulse response identification in the context of implicit model representations recently used as basis for many data-driven simulation and control methods. Precisely, the FIR model considered consists of a linear combination of the columns of a data matrix. An optimal combination for the case of noisy data was recently proposed using a maximum likelihood approach, and the objective here is to optimize the input entries of the data matrix such that the mean-square error matrix of the estimate is minimized. A least-norm problem is derived, which is shown to solve all the classic A-, D-, and E-optimality criteria typically considered in the experiment design literature. Numerical results finally showcase the improved estimation fit achieved with the optimized input.