| 1. |
- Yue, Zuogong, et al.
(författare)
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System Aliasing in Dynamic Network Reconstruction : Issues on Low Sampling Frequencies
- 2021
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Ingår i: IEEE Transactions on Automatic Control. - Piscataway : IEEE. - 0018-9286 .- 1558-2523. ; 66:12, s. 5788-5801
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Tidskriftsartikel (refereegranskat)abstract
- Network reconstruction of dynamical continuous-time (CT) systems is motivated by applications in many fields. Due to experimental limitations, especially in biology, data can be sampled at low frequencies, leading to significant challenges in network inference. We introduce the concept of "system aliasing" and characterize the minimal sampling frequency that allows reconstruction of CT systems from low sampled data. A test criterion is also proposed to detect the presence of system aliasing. With no system aliasing, the paper provides an algorithm to reconstruct dynamic networks from full-state measurements in the presence of noise. With system aliasing, we add additional prior information such as sparsity to overcome the lack of identifiability. This paper opens new directions in modelling of network systems where samples have significant costs. Such tools are essential to process available data in applications subject to experimental limitations. © 2020, IEEE
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| 2. |
- Gumussoy, Suat, et al.
(författare)
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Improving Linear State-Space Models with Additional Iterations
- 2018
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Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 51:15, s. 341-346
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Konferensbidrag (refereegranskat)abstract
- An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation can be improved by subsequent re-estimation of the B, C, and D matrices (which involves linear estimation problems). Several tests are performed, which show that it is generally advisable to do such further re-estimation steps using the maximum likelihood criterion. Stated more succinctly in terms of MATLAB® functions, ssest generally outperforms n4sid.
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| 3. |
- Yue, Zuogong, et al.
(författare)
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Dynamic network reconstruction from heterogeneous datasets
- 2021
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Ingår i: Automatica. - Amsterdam : Elsevier. - 0005-1098 .- 1873-2836. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations of parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data simultaneously to infer the underlying dynamic network. This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that the underlying networks share the same Boolean structure across all experiments. Parametric models are derived for dynamical structure functions, which describe causal interactions between measured variables. Multiple datasets are integrated into one regression problem with additional demands on group sparsity to assure network sparsity and structure consistency. To acquire structured group sparsity, we propose a sampling-based method, together with extended versions of l1-methods and sparse Bayesian learning. The performance of the proposed methods is benchmarked in numerical simulation. In summary, this paper presents efficient methods on network reconstruction from multiple experiments, and reveals practical experience that could guide applications. © 2020 Elsevier Ltd.
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| 4. |
- Abrahamsson, Tomas, et al.
(författare)
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A Study of some Approaches to Vibration Data Analysis
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Using data from extensive vibrational tests of the new aircraft Saab 2000 three different methods for vibration analysis are studied. These methods are ERA (eigensystem realization algorithm), N4SID (a subspace method) and PEM (prediction error approach). We find that both the ERA and N4SID methods give good initial model parameter estimates that can be further improved by the use of PEM. We also find that all methods give good insights into the vibrational modes.
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| 5. |
- Adib Yaghmaie, Farnaz, et al.
(författare)
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Linear Quadratic Control Using Model-Free Reinforcement Learning
- 2023
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9286 .- 1558-2523. ; 68:2, s. 737-752
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we consider linear quadratic (LQ) control problem with process and measurement noises. We analyze the LQ problem in terms of the average cost and the structure of the value function. We assume that the dynamics of the linear system is unknown and only noisy measurements of the state variable are available. Using noisy measurements of the state variable, we propose two model-free iterative algorithms to solve the LQ problem. The proposed algorithms are variants of policy iteration routine where the policy is greedy with respect to the average of all previous iterations. We rigorously analyze the properties of the proposed algorithms, including stability of the generated controllers and convergence. We analyze the effect of measurement noise on the performance of the proposed algorithms, the classical off-policy, and the classical Q-learning routines. We also investigate a model-building approach, inspired by adaptive control, where a model of the dynamical system is estimated and the optimal control problem is solved assuming that the estimated model is the true model. We use a benchmark to evaluate and compare our proposed algorithms with the classical off-policy, the classical Q-learning, and the policy gradient. We show that our model-building approach performs nearly identical to the analytical solution and our proposed policy iteration based algorithms outperform the classical off-policy and the classical Q-learning algorithms on this benchmark but do not outperform the model-building approach.
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| 6. |
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| 7. |
- Akcay, H., et al.
(författare)
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On the choice of norms in system identification
- 1996
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Ingår i: IEEE Transactions on Automatic Control. - Linköping : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286. ; 41:9, s. 1367-1372
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C > 0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all ℓp-norms, p ≀ 2 < ∞ for F(C). ©1996 IEEE.
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| 8. |
- Akçay, Hüseyin, et al.
(författare)
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On the Choice of Norms in System Identification
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 103-108
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C>0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all lp-norms, p⩽2<∞ for F(C).
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| 9. |
- Aljanaideh, Khaled F., et al.
(författare)
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New Features in the System Identification Toolbox - Rapprochements with Machine Learning
- 2021
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Ingår i: IFAC PAPERSONLINE. - : ELSEVIER. - 2405-8963. ; , s. 369-373
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Konferensbidrag (refereegranskat)abstract
- The R2021b release of the System Identification ToolboxTM for MATLAB contains new features that enable the use of machine learning techniques for nonlinear system identification. With this release it is possible to build nonlinear ARX models with regression tree ensemble and Gaussian process regression mapping functions. The release contains several other enhancements including, but not limited to, (a) online state estimation using the extended Kalman filter and the unscented Kalman filter with code generation capability; (b) improved handling of initial conditions for transfer functions and polynomial models; (c) a new architecture of nonlinear black-box models that streamlines regressor handling, reduces memory footprint and improves numerical accuracy; and (d) easy incorporation of identification apps in teaching tools and interactive examples by leveraging the Live Editor tasks of MATLAB. Copyright (C) 2021 The Authors.
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| 10. |
- Andersson, Magnus
(författare)
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Experimental Design and Updating of Finite Element Models
- 1997
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with two partly related topics: model updating and actuator/sensor placement concerning finite element (FE) models of large, flexible mechanical structures.The importance of accurate dynamical FE models of mechanical structures in, e.g., aviation/aerospace applications are steadily increasing. For instance, a sufficient accurate model may reduce the expenses for ground vibration testing and wind-tunnel experiments substantially. It is therefore of high industrial interest to obtain accurate models of flexible structures. One approach is to improve a parameterized, initial FE model using measurements of the real structure, so-called model updating. For a fast, successful model updating, three requirements must be fulfilled. The model updating must be computationally cheap, which requires an efficient model reduction technique. The cost function describing the deviation between the model output and the measurements must have good convexity properties so that an estimation of the parameters corresponding to the global optimum is likely to be obtained. Finally, the optimization methods must be reliable. A novel mode-pairing free cost function is presented, and together with a proposed general procedure for model updating, a cheap model updating formulation with good parameter estimation properties is obtained.Actuator and sensor placement is a part of the experimental design. It is performed in advance of the vibrational experiment in order to ensure high quality measurements. Using a nominal FE model of the structure, an actuator/sensor placement can be made. Actuator/sensor placement tasks are generally discrete, non-convex optimization problems of high complexity. One is therefore restricted to the use of sub-optimal algorithms in order to fulfill time and memory storage requirements. A computationally cheap algorithm for general actuator/sensor placement objectives are proposed. A generalization of an actuator/sensor placement criterion for model updating, and a novel noise-robust actuator placement criterion for experimental modal analysis are proposed.
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