| 1. |
- Korniienko, A., et al.
(författare)
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Hierarchical Robust Analysis for Identified Systems in Network
- 2018
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Ingår i: IFAC PAPERSONLINE. - : ELSEVIER SCIENCE BV. - 2405-8963. ; , s. 383-389
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Konferensbidrag (refereegranskat)abstract
- This paper considers worst-case robustness analysis of a network of locally controlled uncertain systems with uncertain parameter vectors belonging to the ellipsoid sets found by identification procedures. In order to deal with computational complexity of large-scale systems, an hierarchical robustness analysis approach is adapted to these uncertain parameter vectors thus addressing the trade-off between the computation time and the conservatism of the result.
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| 2. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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Outlier-robust estimation of uncertain-input systems with applications to nonparametric FIR and hammerstein models
- 2018
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Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 2475-1456. ; 2:4, s. 647-652
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we present an extension of the class of uncertain-input models to handle cases of measurements with outliers. The general uncertain-input model framework allows us to treat system identification problems in which a linear system, represented by its impulse response, is subject to an input about which we have partial information. Both the impulse response and the input are modeled as Gaussian processes and the kernels are used to encode the information available. The whole model is then estimated using an approximate empirical Bayes approach. We extend the uncertain-input model framework to non-Gaussian measurement models by considering the noise precisions as realizations of a Gamma prior. We validate the approach on a dataset of linear systems and on a dataset of Hammerstein systems where the measurements are corrupted by outliers.
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| 3. |
- Zhang, Liang, et al.
(författare)
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Control of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE
- 2021
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Ingår i: Metabolic engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 65, s. 135-145
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Tidskriftsartikel (refereegranskat)abstract
- The N-linked glycosylation pattern is an important quality attribute of therapeutic glycoproteins. It has been reported by our group and by others that different carbon sources, such as glucose, mannose and galactose, can differently impact the glycosylation profile of glycoproteins in mammalian cell culture. Acting on the sugar feeding is thus an attractive strategy to tune the glycan pattern. However, in case of feeding of more than one carbon source simultaneously, the cells give priority to the one with the highest uptake rate, which limits the usage of this tuning, e.g. the cells favor consuming glucose in comparison to galactose. We present here a new feeding strategy (named ‘TAFE’ for targeted feeding) for perfusion culture to adjust the concentrations of fed sugars influencing the glycosylation. The strategy consists in setting the sugar feeding such that the cells are forced to consume these substrates at a target cell specific consumption rate decided by the operator and taking into account the cell specific perfusion rate (CSPR). This strategy is applied in perfusion cultures of Chinese hamster ovary (CHO) cells, illustrated by ten different regimes of sugar feeding, including glucose, galactose and mannose. Applying the TAFE strategy, different glycan profiles were obtained using the different feeding regimes. Furthermore, we successfully forced the cells to consume higher proportions of non-glucose sugars, which have lower transport rates than glucose in presence of this latter, in a controlled way. In previous work, a mathematical model named Glycan Residues Balance Analysis (GReBA) was developed to model the glycosylation profile based on the fed carbon sources. The present data were applied to the GReBA to design a feeding regime targeting a given glycosylation profile. The ability of the model to achieve this objective was confirmed by a multi-round of leave-one-out cross-validation (LOOCV), leading to the conclusion that the GReBA model can be used to design the feeding regime of a perfusion cell culture to obtain a desired glycosylation profile.
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| 4. |
- Abdalmoaty, Mohamed, 1986-
(författare)
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Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.
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| 5. |
- Colin, Kevin, et al.
(författare)
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Regret Minimization for Linear Quadratic Adaptive Controllers Using Fisher Feedback Exploration
- 2022
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Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 6, s. 2870-2875
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we study the trade-off between exploration and exploitation for linear quadratic adaptive control. This trade-off can be expressed as a function of the exploration and exploitation costs, called cumulative regret. It has been shown over the years that the optimal asymptotic rate of the cumulative regret is in many instances O(root T). In particular, this rate can be obtained by adding a white noise external excitation, with a variance decaying as O(1/root T). As the amount of excitation is pre-determined, such approaches can be viewed as open loop control of the external excitation. In this contribution, we approach the problem of designing the external excitation from a feedback perspective leveraging the well known benefits of feedback control for decreasing sensitivity to external disturbances and system-model mismatch, as compared to open loop strategies. We base the feedback on the Fisher information matrix which is a measure of the accuracy of the model. Specifically, the amplitude of the exploration signal is seen as the control input while the minimum eigenvalue of the Fisher matrix is the variable to be controlled. We call such exploration strategies Fisher Feedback Exploration (F2E). We propose one explicit F2E design, called Inverse Fisher Feedback Exploration (IF2E), and argue that this design guarantees the optimal asymptotic rate for the cumulative regret. We provide theoretical support for IF2E and in a numerical example we illustrate benefits of IF2E and compare it with the open loop approach as well as a method based on Thompson sampling.
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| 6. |
- Ghosh, Anubhab, et al.
(författare)
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DeepBayes—An estimator for parameter estimation in stochastic nonlinear dynamical models
- 2024
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Ingår i: Automatica. - : Elsevier Ltd. - 0005-1098 .- 1873-2836. ; 159
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Tidskriftsartikel (refereegranskat)abstract
- Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks — long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator.
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| 7. |
- Kreiberg, David, 1971-
(författare)
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A covariance structure analysis approach to the errors-in-variables estimation problem
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- It is a well-known fact that standard regression techniques, when applied to errors-in-variables (EIV) models, lead to biased and inconsistent parameter estimation. The work presented in this thesis address the EIV estimation problem using covariance structure analysis (CSA). When performing CSA, the standard implementation of the minimum distance (MD) estimator is to apply computationally demanding nonlinear least squares (NLLS). This thesis provides a solution to this problem by proposing a computationally less demanding separable nonlinear least squares (SNLLS) implementation of the estimator.The thesis consists of four papers. The first paper presents a covariance matching (CM) approach for identifying the single-input single-output (SISO) EIV model. The outlined approach extends previous known results by deriving an asymptotic covariance matrix of the jointly estimated system parameters, noise variances and auxiliary parameters. The second paper introduces two formulations of the SISO EIV model using structural equation modeling (SEM). The two formulations allow for quick implementation using standard SEM-based software. The third paper propose a numerically more efficient implementation of the MD estimator for estimating confirmatory factor analysis (CFA) models. The implementation uses an SNLLS approach, which allows part of the parameter vector to be estimated using numerically efficient linear techniques. The fourth and final paper presents a CFA-EIV modeling approach that allows for colored output noise. The presentation extends previous work by including a detailed treatment of the theoretical aspects of the MD estimator. All four papers use simulation examples to illustrate the outlined procedures.
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| 8. |
- Olofsson, K. Erik J., et al.
(författare)
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Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response
- 2011
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Ingår i: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 0741-3335 .- 1361-6587. ; 53:8, s. 084003-
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Tidskriftsartikel (refereegranskat)abstract
- The usage of computationally feasible overparametrized and nonregularized system identification signal processing methods is assessed for automated determination of the full reversed-field pinch external plasma response spectrum for the experiment EXTRAP T2R. No assumptions on the geometry of eigenmodes are imposed. The attempted approach consists of high-order autoregressive exogenous estimation followed by Markov block coefficient construction and Hankel matrix singular value decomposition. It is seen that the obtained 'black-box' state-space models indeed can be compared with the commonplace ideal magnetohydrodynamics (MHD) resistive thin-shell model in cylindrical geometry. It is possible to directly map the most unstable autodetected empirical system pole to the corresponding theoretical resistive shell MHD eigenmode.
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| 9. |
- Parsa, Javad, et al.
(författare)
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Application-Oriented Input Design With Low Coherence Constraint
- 2023
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Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 7, s. 193-198
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Tidskriftsartikel (refereegranskat)abstract
- In optimal input design input sequences are typically generated without paying attention to the correlations between the regressors of the model to be estimated. In fact, in many cases high correlations are beneficial. This is in contrast to the requirements in sparse estimation. Mutual coherence is the maximum of these correlations, and in case the parameter vector is known to be sparse, we need a low mutual coherence in order to estimate it accurately. This contribution proposes adding a constraint on the mutual coherence to the optimal input design problem to improve the accuracy of estimated sparse models. The proposed method can be combined with any sparse estimation algorithm to estimate the parameters of a model. However, we focus in particular on the bound on the mutual coherence required for Orthogonal Matching Pursuit (OMP), a well-known algorithm in sparse estimation. Furthermore, we analyze the effect of the proposed method on the required input power. Finally, we evaluate, in a numerical study, the performance of the proposed method compared to state-of-the-art algorithms for input design.
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| 10. |
- Parsa, Javad, et al.
(författare)
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Optimal Input Design for Sparse System Identification
- 2022
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Ingår i: 2022 EUROPEAN CONTROL CONFERENCE (ECC). - : IEEE. ; , s. 1999-2004
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Konferensbidrag (refereegranskat)abstract
- In this contribution we consider sparse linear regression problems. It is well known that the mutual coherence, i.e. the maximum correlation of the regressors, is important for the ability of any algorithm to recover the sparsity pattern of an unknown parameter vector from data. A low mutual coherence improves the ability of recovery. In optimal experiment design this requirement may be in conflict with other objectives encoded by the desired Fisher matrix. In this contribution we alleviate this issue by combining optimal input design with a recently proposed approach to achieve low mutual coherence by way of a linear coordinate transformation. The resulting optimization problem is solved using cyclic minimization. Via simulations we demonstrate that the resulting algorithm is able to achieve a Fisher matrix which results in a performance close to the performance if the sparsity would have been known, while at the same time being able to recover the sparsity pattern.
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