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This paper proposes a non-iterative direct data-driven technique that deals with linear time-invariant (LTI) controller design by directly identifying the controller from input-output data without using plant identification. We define the feasible controller parameter set and formulate the problem of designing a controller to match the behaviour of a given reference model as an equivalent set-membership errors-in-variables problem. Then we design the controller parameters by applying recent results in set-membership errors-in-variables identification. Finally, we analyse the effectiveness of the presented technique through simulation examples and experimental results.

This paper studies the expectile regression with error-in-variables to reduce the data error and describe the overall data distribution. Specifically, the asymptotic normality of the proposed estimator is thoroughly investigated, and an IRWLS algorithm based on orthogonal distance expectile regression (ODER) is proposed to estimate the parameters. Extensive simulation studies and real data applications evaluate our method’s capabilities in reducing the measurement error bias, demonstrating our model’s parameter estimation effectiveness, and its capability in reducing the simulation error compared with linear and quantile regression schemes.

In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed. We propose an Expectation-Maximization-based algorithm to estimate the system model parameters, the input and output noise variances, and the Gaussian mixture noise-free input parameters. The benefits of our proposal are illustrated via numerical simulations.

Estimating the impact of a treatment on a given response is needed in many biomedical applications. However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments is unknown. We introduce a novel method for this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model accounts for errors not only in treatment covariates, but also in treatment timings, a problem arising in practice for example when data on treatments are based on user self-reporting. We validate our model with simulated and real patient data, and show that in a challenging application of estimating the impact of diet on continuous blood glucose measurements, accounting for measurement error significantly improves estimation and prediction accuracy. [ABSTRACT FROM AUTHOR]

The classical time-invariance assumption is often not (exactly) met in real-life applications. As a natural extension of the frequency response function (FRF), the time-variant FRF (TV-FRF) provides quick insight into the complex dynamics of time-variant systems. Recently, a procedure has been proposed to estimate nonparametrically the TV-FRF from known input, noisy output measurements of time-variant systems operating in open loop. However, in many applications, feedback is present either due to an explicit control action or due to the interaction between a nonideal actuator and the system under test. The extension of the open-loop approach to noisy input, noisy output measurements of time-variant systems operating in closed loop requires the deconvolution of the time-variant impulse response of the cascade of two time-variant systems. In this paper, this nontrivial problem is solved for a particular class of time-variant systems. The robustness of the approach with respect to the system assumption is demonstrated via simulations and measurements on an electronic circuit. [ABSTRACT FROM PUBLISHER]

Frequency response function (FRF) measurements are very often used to get a quick insight into the dynamic behavior of complex systems; even if it is known that these systems are only approximately linear and time-invariant. Therefore, it is important to detect and quantify the deviation from the ideal linear time-invariant framework that is inherent to the concept of an FRF. This paper presents a method to detect and quantify the nonlinear and time-variant effects in FRF measurements using periodic excitations. The proposed method can handle noisy input, noisy output data, and nonlinear time-variant systems operating in feedback. [ABSTRACT FROM PUBLISHER]

In this paper, we study the asymptotic properties (consistency and asymptotic efficiency) of a frequency-domain errors-in-variables estimator using data extracted from overlapping subrecords. While the classical approach without overlap needs six consecutive periods, we show in this paper that, using overlapping subrecords, consistent estimators can be found with only two periods of the steady state response to a periodic excitation. This is done using overlapping subrecords. Moreover, the system identification procedure developed for data extracted from independent periods is shown to be valid for data extracted from overlapping subrecords. This allows the user to reduce the measurement time considerably without changing the identification procedure. [ABSTRACT FROM PUBLISHER]

System identification with noisy input-output measurements has been dominantly addressed through the optimization of the mean-squared-error criterion (MSE), especially in adaptive filtering. MSE is known to provide models that approximate the conditional expectation of the target output given the input; however, when the input signal is also contaminated by noise--a frequent occurrence--MSE yields biased estimates of the model parameters with the severity of the bias dependent on the noise power. This drawback has been addressed in various ways, including errors-in-variables techniques. Recently, error whitening criterion (EWC) and associated adaptation algorithms were proposed to address this issue in linear system identification. We extend the applicability of the main concept behind EWC to the unbiased identification of order-2 Volterra series models of nonlinear dynamical systems. The extension does not apply to higher order Volterra models. The main contribution of this letter is a statistical criterion that can be utilized to identify analytically the true parameters of an order-2 Volterra model from noisy input-output data. We also support the theoretical results with simulations; however online learning algorithms that can be derived for the proposed criterion will not be addressed. [ABSTRACT FROM AUTHOR]

In this paper, we study the consistency of a frequency-domain, errors-in-variables estimator using data extracted from overlapping subrecords. While the classical approach without overlap needs six consecutive periods, we show in this paper that by using overlapping subrecords, consistent models can be found with only two periods of the steady-state response of a periodic excitation. Moreover, the system identification procedure used for data extracted from independent experiments is shown to be valid for data extracted from overlapping subrecords. This allows the user to considerably reduce the measurement time or the measurement uncertainty without changing the identification procedure. [ABSTRACT FROM AUTHOR]

Identification of parametric transfer-function models from noisy input/output observations is an important task in many engineering applications. Aside from the parametric model, the estimation algorithm used should also provide accurate confidence bounds. In addition, it is important to know whether the proposed estimation algorithm has the lowest possible uncertainty within the class of consistent estimators. This paper handles these issues for the frequency-domain Gaussian maximum-likelihood estimator of rational transfer-function models within an errors-in-variables framework. [ABSTRACT FROM AUTHOR]

This letter deals with the identification of time-varying Rayleigh fading channels using a training sequence-based approach. When the fading channel is approximated by an autoregressive (AR) process, it can be estimated by means of Kalman filtering, for instance. However, this method requires the estimations of both the AR parameters and the noise variances in the state-space representation of the system. For this purpose, the existing noise compensated approaches could be considered, but they usually require a long observation window and do not necessarily provide reliable estimates when the signal-to-noise ratio is low. Therefore, we propose to view the channel identification as an errors-in-variables (EIV) issue. The method consists in searching the noise variances that enable specific noise compensated autocorrelation matrices of observations to be positive semidefinite. In addition, the AR parameters can be estimated from the null spaces of these matrices. Simulation results confirm the effectiveness of this approach, especially in presence of a high amount of noise. [ABSTRACT FROM AUTHOR]