| 1. |
- Abdalmoaty, Mohamed, 1986-, et al.
(author)
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The Gaussian MLE versus the Optimally weighted LSE
- 2020
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In: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 37:6, s. 195-199
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Journal article (peer-reviewed)abstract
- In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.
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| 2. |
- Altafini, Claudio, et al.
(author)
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A feedback control scheme for reversing a truck and trailer vehicle
- 2001
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In: IEEE transactions on robotics and automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1042-296X. ; 17:6, s. 915-922
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Journal article (peer-reviewed)abstract
- A control scheme is proposed for stabilization of backward driving along simple paths for a miniaturized vehicle composed of a truck and a two-axle trailer. The paths chosen are straight lines and arcs of circles. When reversing, the truck and trailer under examination can be modeled as an unstable nonlinear system with state and input saturations. The simplified goal of stabilizing along a trajectory (instead of a point) allows us to consider a system with controllable linearization. Still, the combination of instability and saturations makes the task impossible with a single controller. In fact, the system cannot be driven backward from all initial states because of the jack-knife effects between the parts of the multibody vehicle; it is sometimes necessary to drive forward to enter into a specific region of attraction. This leads to the use of hybrid controllers. The scheme has been implemented and successfully used to reverse the radio-controlled vehicle.
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| 3. |
- Andersson, Olov, 1979-, et al.
(author)
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WARA-PS : a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation
- 2021
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In: Autonomous Intelligent Systems. - : Springer Nature. - 2730-616X. ; 1:1
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Journal article (peer-reviewed)abstract
- A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.
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| 4. |
- Andersson, Sören, et al.
(author)
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An adaptive array for mobile communication systems
- 1991
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In: IEEE Transactions on Vehicular Technology. - Toronto, Ont, Can : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 40:1 pt 2, s. 230-236, s. 3289-3292
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Journal article (peer-reviewed)abstract
- The use of adaptive antenna techniques to increase the channel capacity is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting modes. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction-finding followed by optimal combination of the antenna outputs. Comparison with a method based on reference signals is made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.
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| 5. |
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| 6. |
- Avventi, Enrico, et al.
(author)
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Graphical Models of Autoregressive Moving-Average Processes
- 2010
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In: The 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010).
-
Conference paper (peer-reviewed)abstract
- Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework which also spreads further light on previous approaches and results.
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| 7. |
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| 8. |
- Barenthin, Märta, et al.
(author)
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Data-driven methods for L2-gain estimation
- 2009
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In: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 1597-1602
-
Conference paper (peer-reviewed)abstract
- In this paper we present and discuss some data-driven methods for estimation of the L2-gain of dynamical systems. Partial results on convergence and statistical properties are provided. The methods are based on multiple experiments on the system. The main idea is to directly estimate the maximizing input signal by using iterative experiments on the true system. We study such a data-driven method based on a stochastic gradient method. We show that this method is very closely related to the so-called power iteration method based on the power method in numerical analysis. Furthermore, it is shown that this method is applicable for linear systems with noisy measurements. We will also study L2-gain estimation of Hammerstein systems. The stochastic gradient method and the power iteration method are evaluated and compared in simulation examples. © 2009 IFAC.
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| 9. |
- Barenthin, Märta, et al.
(author)
-
Gain estimation for Hammerstein systems
- 2006
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In: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 784-789
-
Conference paper (peer-reviewed)abstract
- In this paper, we discuss and compare three different approaches for L2- gain estimation of Hammerstein systems. The objective is to find the input signal that maximizes the gain. A fundamental difference between two of the approaches is the class, or structure, of the input signals. The first approach involves describing functions and therefore the class of input signals is sinusoids. In this case we assume that we have a model of the system and we search for the amplitude and frequency that give the largest gain. In the second approach, no structure on the input signal is assumed in advance and the system does not have to be modelled first. The maximizing input is found using an iterative procedure called power iterations. In the last approach, a new iterative procedure tailored for memoryless nonlinearities is used to find the maximizing input for the unmodelled nonlinear part of the Hammerstein system. The approaches are illustrated by numerical examples.
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| 10. |
- Barenthin, Märta, et al.
(author)
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Validation of stability for an induction machine drive using power iterations
- 2005
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In: Proceedings of the 16th IFAC World Congress, 2005. - Prague. - 9783902661753 ; , s. 892-897
-
Conference paper (peer-reviewed)abstract
- This work is an extension of the paper (Mosskull et al., 2003), in which the modelling, identification and stability of an nonlinear induction machine drive is studied. The validation of the stability margins of the system is refined by an improved estimate of the induced L2 loop gain of the system. This is done with a procedure called power iterations where input sequences suitable for estimating the gain are generated iteratively through experiments on the system. The power iterations result in higher gain estimates compared to the experiments previously presented. This implies that more accurate estimates are obtained as, in general, only lower bounds can be obtained as estimates for the gain. The new gain estimates are well below one, which suggests that the feedback system is stable. The experiments are performed on an industrial hardware/software simulation platform. in this paper we also discuss the power iterations from a more general point of view. The usefulness of the method for gain estimation of nonlinear systems is illustrated through simulation examples. The basic principles of the method are provided.
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| 11. |
- Bereza-Jarocinski, Robert, et al.
(author)
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Distributed Model Predictive Control for Cooperative Landing
- 2020
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In: Proceedings 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges. - : Elsevier BV. ; , s. 15180-15185
-
Conference paper (peer-reviewed)abstract
- We design, implement and test two control algorithms for autonomously landing a drone on an autonomous boat. The first algorithm uses distributed model predictive control (DMPC), while the second combines a cascade controller with DMPC. The algorithms are implemented on a real drone, while the boat's motion is simulated, and their performance is compared to a centralized model predictive controller. Field experiments are performed, where all algorithms show an ability to land while avoiding violation of the safety constraints. The two distributed algorithms further show the ability to use longer prediction horizons than the centralized model predictive controller, especially in the cascade case, and also demonstrate improved robustness towards breaks in communication.
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| 12. |
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| 13. |
- Blomqvist, Anders, et al.
(author)
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On frequency weighting in autoregressive spectral estimation
- 2005
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In: IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 0780388747 ; , s. 245-248
-
Conference paper (peer-reviewed)abstract
- This paper treats the problem of approximating a complex stochastic process in a given frequency region by an estimated autoregressive (AR) model. Two frequency domain approaches are discussed: a weighted frequency domain maximum likelihood method and a prefiltered covariance extension method based on the theory of Lindquist and co-workers. It is shown that these two approaches are very closely related and can both be formulated as convex optimization problems. An examples illustrating the methods and the effect of prefiltering/weighting is provided. The results show that these methods are capable of tuning the AR model fit to a specified frequency region.
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| 14. |
- Blomqvist, Anders, et al.
(author)
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On the relation between weighted frequency-domain maximum-likelihood power spectral estimation and the prefiltered covariance extension approach
- 2007
-
In: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 55:1, s. 384-389
-
Journal article (peer-reviewed)abstract
- The aim of this correspondence is to study the connection between weighted frequency-domain maximum-likelihood power spectral estimation and the time-domain prefiltered covariance extension approach. Weighting and prefiltering are introduced to emphasize the model fit in a certain frequency range. The main result is that these two methods are very closely related for the case of autoregressive (AR) model estimation, which implies that both can be formulated as convex optimization problems. Examples illustrating the methods and the effect of prefiltering/weighting are provided.
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| 15. |
- Bodin, Per, et al.
(author)
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A frequency response estimation method based on smoothing and thresholding
- 1998
-
In: International Journal of Adaptive Control and Signal Processing. - 0890-6327 .- 1099-1115. ; 12:5, s. 407-416
-
Journal article (peer-reviewed)abstract
- A standard approach for estimating the frequency function of a linear dynamical system is to use spectral estimation. Traditionally, this is done by smoothing the noisy frequency data using linear filters. The method has proved to be successful in most cases and is widely used. However, if the frequency response has fine details appearing only locally in frequency, the loss of resolution caused by smoothing might result in unacceptable errors. In this paper, a different method for frequency response estimation is suggested. The method utilizes recently proposed wavelet-based denoising schemes combined with traditional smoothing techniques. The wavelet transform is applied in the frequency domain in order to provide a suitable frequency window. Tested through simulations, this approach provides an alternative when traditional methods fail.
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| 16. |
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| 17. |
- Bodin, Per, et al.
(author)
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Algorithm for selection of best orthonormal rational basis
- 1997
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In: Proceedings of the IEEE Conference on Decision and Control. - San Diego, CA, USA. ; , s. 1277-1282
-
Conference paper (peer-reviewed)abstract
- This contribution deals with the problem of structure determination for generalized orthonormal basis models used in system identification. The model structure is parameterized by a pre-specified set of poles. Given this structure and experimental data a model can be estimated using linear regression techniques. Since the variance of the estimated model increases with the number of estimated parameters, the objective is to find structures that are as compact/parsimonious as possible. A natural approach would be to estimate the poles, but this leads to nonlinear optimization with possible local minima. In this paper, a best basis algorithm is derived for the generalized orthonormal rational bases. Combined with linear regression and thresholding this leads to compact transfer function representations.
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| 18. |
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| 19. |
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| 20. |
- Bodin, Per, et al.
(author)
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Selection of best orthonormal rational basis
- 2000
-
In: SIAM Journal on Control and Optimization. - 0363-0129 .- 1095-7138. ; 38:4, s. 995-1032
-
Journal article (peer-reviewed)abstract
- This contribution deals with the problem of structure determination for generalized orthonormal basis models used in system identification. The model structure is parameterized by a prespecified set of poles representing a finite-dimensional subspace of H2. Given this structure and experimental data, a model can be estimated using linear regression techniques. Since the variance of the estimated model increases with the number of estimated parameters, one objective is to find coordinates, or a basis, for the finite-dimensional subspace giving as compact or parsimonious a system representation as possible. In this paper, a best basis algorithm and a coefficient decomposition scheme are derived for the generalized orthonormal rational bases. Combined with linear regression and thresholding this leads to compact transfer function representations. The methods are demonstrated with several examples.
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| 21. |
- Bodin, Per, et al.
(author)
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Thresholding in high order transfer function estimation
- 1994
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In: Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on. ; , s. 3400-3405
-
Conference paper (peer-reviewed)abstract
- A problem in prediction error system identification methods is estimation of pole locations. Typically, iterative numerical optimization methods are used. Reliable initial values are then necessary for good results. The parameterization is often done in the coefficients of transfer function polynomials or some canonical form. In this contribution we discuss a couple of issues related to the above problem. First, we study how all-pass systems can be used to generate suitable model structures. This analysis is based on the relation between balanced realizations of all-pass filters and orthonormal basis transfer functions. Next, we investigate the effects of a priori fixed pole locations, such as in Laguerre and Kautz models. One idea is to use very flexible high-order models. However, the corresponding estimation problem has to be regularized in order to reduce the variance errors due to noise. We will discuss how this can be done by using thresholding of the estimated coefficients
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| 22. |
- Brighenti, C., et al.
(author)
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Input design using Markov chains for system identification
- 2009
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In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. - : IEEE conference proceedings. ; , s. 1557-1562
-
Conference paper (peer-reviewed)abstract
- This paper studies the input design problem for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the system. This allows to directly include input amplitude constraints in the input model by properly choosing the state space of the Markov chain, which is defined so that the Markov chain generates a multi-level sequence. The probability distribution of the Markov chain is shaped in order to minimize the cost function considered in the input design problem. Stochastic approximation is used to minimize that cost function. With this approach, the input signal to apply to the system can be easily generated by extracting samples from the optimal distribution. A numerical example shows how this method can improve estimation with respect to other input realization techniques.
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| 23. |
- Carlemairn, Catharina, et al.
(author)
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Low complexity parameter estimation approach for fast time-delay estimation
- 1997
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In: Decision and Control, 1997., Proceedings of the 36th IEEE Conference on. ; , s. 1603-1608
-
Conference paper (peer-reviewed)abstract
- Presents algorithms for time-delay estimation based on a parametric approach. The suggested methods combine an exhaustive search with a low complexity since they do not require filtering or correlation computations. Consequently, the problems with local minima of gradient search algorithms are avoided. The proposed detection schemes are experimentally verified by way of computer simulations. Furthermore, receiver operator characteristics are presented
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| 24. |
- Carlemalm, Catharina, et al.
(author)
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Algorithms for time delay estimation using a low complexity exhaustive search
- 1999
-
In: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 44:5, s. 1031-1037
-
Journal article (peer-reviewed)abstract
- This paper addresses the problem of time-delay estimation. Two new algorithms for time-delay estimation are developed and analyzed. The suggested methods combine an exhaustive search with a low complexity. Consequently, the problems with local minima of gradient search algorithms are avoided. The receiver operating characteristics (ROC) are computed, and together with simulation results these verify the performance of the estimation schemes.
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| 25. |
- Carlemalm, Catharina, et al.
(author)
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On the problem of blind equalization considering abrupt changes in the channel characteristics
- 2015
-
In: European Signal Processing Conference. - : European Signal Processing Conference, EUSIPCO.
-
Conference paper (peer-reviewed)abstract
- The problem of blind equalization in a digital communication system is considered. Unfortunately, the circuit might suffer from abrupt changes. Thus, it is critical not to ignore this phenomenon when the problem of blind equalization is analyzed. The proposed method, which is based on an Ito stochastic differential calculus approach, describes the dynamics of the output signal with an infinite impulse response (IIR) model where the involved taps are modeled as time-varying cadlag (con-tinu a droite limites a gauche) processes. Therefore, nonlinear and time-variant changes in the channel characteristics are included.
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| 26. |
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| 27. |
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| 28. |
- Collares Pereira, Goncalo, et al.
(author)
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Linear Time-Varying Robust Model Predictive Control for Discrete-Time Nonlinear Systems
- 2018
-
In: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 ; , s. 2659-2666
-
Conference paper (peer-reviewed)abstract
- This paper presents a robust model predictive controller for discrete-time nonlinear systems, subject to state and input constraints and unknown but bounded input disturbances. The prediction model uses a linearized time-varying version of the original discrete-time system. The proposed optimization problem includes the initial state of the current nominal model of the system as an optimization variable, which allows to guarantee robust exponential stability of a disturbance invariant set for the discrete-time nonlinear system. From simulations, it is possible to verify the proposed algorithm is real-time capable, since the problem is convex and posed as a quadratic program.
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| 29. |
- de Miranda de Matos Lourenço, Inês, 1994-
(author)
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Learning from Interactions : Forward and Inverse Decision-Making for Autonomous Dynamical Systems
- 2023
-
Doctoral thesis (other academic/artistic)abstract
- Decision-making is the mechanism of using available information to generate solutions to given problems by forming preferences, beliefs, and selecting courses of action amongst several alternatives. In this thesis, we study the mechanisms that generate behavior (the forward problem) and how their characteristics can explain observed behavior (the inverse problem). Both problems play a pivotal role in contemporary research due to the desire to design sophisticated autonomous agents that serve as the building blocks for a smart society, amidst complexity, risk, and uncertainty. This work explores different parts of the autonomous decision-making process where agents learn from interacting with each other and the environment that surrounds them. We address fundamental problems of behavior modeling, parameter estimation in the form of beliefs, distributions, and reward functions, and then finally interactions with other agents; which lay the foundation for a complete and integrative framework for decision-making and learning. The thesis is divided into four parts, each featuring a different information exchange paradigm.First, we model the forward problem of how a decision-maker forms beliefs about the world and the inverse problem of estimating these beliefs from the agent’s behavior. The private belief (posterior distribution) on the state of the world is formed according to a hidden Markov model by filtering private information. The ability to estimate private beliefs forms a foundation for predicting and counteracting against future actions. We answer the problems of i) how the private belief of the decision-maker can be estimated by observing its decisions (under two different scenarios), and ii) how the decision-maker can protect its private belief from an adversary by confusing it. We exemplify the applicability of our frameworks in regime-switching Markovian portfolio allocation.In the second part, we study forward decision-making of biological systems and the inverse problem of how to obtain insight into their intrinsic characteristics. We focus on time perception – how humans and animals perceive the passage of time – and design a biologically-inspired decision-making framework using reinforcement learning that replicates timing mechanisms. We show that a simulated robot equipped with our framework is able to perceive time similarly to animals, and that by analyzing its performed actions we are able to estimate the parameters of timing mechanisms.Next, we consider teacher-student settings where a teacher agent can intervene with the decision-making process of a student agent to assist it in performing a task. In the third part, we propose correctional learning as an approach where the teacher can intercept the observations the student collects from the system and modify them to improve the estimation process of the student. We provide finite-sample results for batch correctional learning in system identification, generalize it to more complex systems using optimal transport, and lower-bound improvements on the estimate’s variance for the online case.Decision-making in teacher-student settings like the previous one requires both agents to have aligned models of understanding of each other. In the fourth and last part of this thesis, the teacher can, instead, alter the decisions of the decision-maker in a human-robot interaction setting. We use a confidence-based misalignment detection method that enables the robot to update its knowledge proportionally to its confidence in the human corrections and propose a framework to disambiguate between misalignment caused by incorrectly learned features that do not generalize to new environments and features entirely missing from the robot’s model. We demonstrate the proposed framework in a 7 degrees-of-freedom robot manipulator with physical human corrections and show how to initiate the model realignment process once misalignment is detected.
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| 30. |
- Ebadat, Afrooz, et al.
(author)
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Application-oriented input design for room occupancy estimation algorithms
- 2017
-
In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - Piscataway, NJ : IEEE. - 9781509028733 - 9781509028740
-
Conference paper (peer-reviewed)abstract
- We consider the problem of occupancy estimation in buildings using available environmental information. In particular, we study the problem of how to collect data that is informative enough for occupancy estimation purposes. We thus propose an application-oriented input design approach for designing the ventilation signal to be used while collecting the system identification datasets. The main goal of the method is to guarantee a certain accuracy in the estimated occupancy levels while minimizing the experimental time and effort. To take into account potential limitations on the actuation signals we moreover formulate the problem as a recursive nonlinear and nonconvex optimization problem, solved then using exhaustive search methods. We finally corroborate the theoretical findings with some numerical examples, which results show that computing ventilation signals using experiment design concepts leads to occupancy estimator performing 4 times better in terms of Mean Square Error (MSE).
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| 31. |
- Ebadat, Afrooz, et al.
(author)
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Applications Oriented Input Design for Closed-Loop System Identification : a Graph-Theory Approach
- 2014
-
In: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781467360906 ; , s. 4125-4130
-
Conference paper (peer-reviewed)abstract
- A new approach to experimental design for identification of closed-loop models is presented. The method considers the design of an experiment by minimizing experimental cost, subject to probabilistic bounds on the input and output signals, and quality constraints on the identified model. The input and output bounds are common in many industrial processes due to physical limitations of actuators. The aforementioned constraints make the problem non-convex. By assuming that the experiment is a realization of a stationary process with finite memory and finite alphabet, we use results from graph-theory to relax the problem. The key feature of this approach is that the problem becomes convex even for non-linear feedback systems. A numerical example shows that the proposed technique is an attractive alternative for closed-loop system identification.
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| 32. |
- Ebadat, Afrooz, et al.
(author)
-
Model Predictive Control oriented experiment design for system identification : A graph theoretical approach
- 2017
-
In: Journal of Process Control. - : ELSEVIER SCI LTD. - 0959-1524 .- 1873-2771. ; 52, s. 75-84
-
Journal article (peer-reviewed)abstract
- We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.
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| 33. |
- Egidio, Lucas N., et al.
(author)
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Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
- 2021
-
In: 2021 international joint conference on neural networks (IJCNN). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780738133669
-
Conference paper (peer-reviewed)abstract
- We consider the problem to learn a step-size policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. This is a limited computational memory quasi-Newton method widely used for deterministic unconstrained optimization. However, L-BFGS is currently avoided in large-scale problems for requiring step sizes to be provided at each iteration. Current methodologies for the step size selection for L-BFGS use heuristic tuning of design parameters and massive re-evaluations of the objective function and gradient to find appropriate step-lengths. We propose a neural network architecture with local information of the current iterate as the input. The step-length policy is learned from data of similar optimization problems, avoids additional evaluations of the objective function, and guarantees that the output step remains inside a pre-defined interval. The corresponding training procedure is formulated as a stochastic optimization problem using the backpropagation through time algorithm. The performance of the proposed method is evaluated on the training of image classifiers for the MNIST database for handwritten digits and for CIFAR-10. The results show that the proposed algorithm outperforms heuristically tuned optimizers such as ADAM, RMSprop, L-BFGS with a backtracking line search, and L-BFGS with a constant step size. The numerical results also show that a learned policy can be used as a warm-start to train new policies for different problems after a few additional training steps, highlighting its potential use in multiple large-scale optimization problems.
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| 34. |
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| 35. |
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| 36. |
- Finn, Cory K., et al.
(author)
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Constrained predictive control using orthogonal expansions
- 1993
-
In: AIChE Journal. - : Wiley. - 0001-1541 .- 1547-5905. ; 39, s. 1810-1826
-
Journal article (peer-reviewed)abstract
- In this article, we approximate bounded operators by orthogonal expansion. The rate of convergence depends on the choice of basis functions. Markov-Laguerre functions give rapid convergence for open-loop stable systems with long delay. The Markov-Kautz model can be used for lightly damped systems, and a more general orthogonal expansion is developed for modeling multivariable systems with widely scattered poles. The finite impulse response model is a special case of these models. A-priori knowledge about dominant time constants, time delay and oscillatory modes is used to reduce the model complexity and to improve conditioning of the parameter estimation algorithm. Algorithms for predictive control are developed, as well as conditions for constraint compatibility, closed-loop stability and constraint satisfaction for the ideal case. An H8-like design technique proposed guarantees robust stability in the presence of input constraints; output constraints may give ￜchatter.ￜ A chatter-free algorithm is proposed.
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| 37. |
- González, Rodrigo A., 1992-
(author)
-
Consistency and efficiency in continuous-time system identification
- 2020
-
Licentiate thesis (other academic/artistic)abstract
- Continuous-time system identification deals with the problem of building continuous-time models of dynamical systems from sampled input and output data. In this field, there are two main approaches: indirect and direct. In the indirect approach, a suitable discrete-time model is first determined, and then it is transformed into continuous-time. On the other hand, the direct approach obtains a continuous-time model directly from the sampled data. In both approaches there exists a dichotomy between discrete-time data and continuous-time models, which can induce robustness issues and complications in the theoretical analysis of identification algorithms. These difficulties are addressed in this thesis.First, we consider the indirect approach to continuous-time system identification. For a zero-order hold sampling mechanism, this approach usually leads to a transfer function estimate with relative degree one, independent of the relative degree of the strictly proper true system. Inspired by the indirect prediction error method, we propose an indirect-approach estimator that enforces the desired number of poles and zeros in the continuous-time transfer function estimate, and show that the estimator is consistent and asymptotically efficient. A robustification of this method is also developed, by which the estimates are also guaranteed to deliver stable models.In the second part of the thesis, we analyze asymptotic properties of the Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC), which is one of the most popular direct identification methods. This algorithm applies an adaptive prefiltering to the sampled input and output that requires assumptions on the intersample behavior of the signals. We present a comprehensive analysis on the consistency and asymptotic efficiency of the SRIVC estimator while taking into account the intersample behavior of the input signal. Our results show that the SRIVC estimator is generically consistent when the intersample behavior of the input is known exactly and subsequently used in the implementation of the algorithm, and we give conditions under which consistency is not achieved. In terms of statistical efficiency, we compute the asymptotic Cramér-Rao lower bound for an output error model structure with Gaussian noise, and derive the asymptotic covariance of the SRIVC estimates. We conclude that the SRIVC estimator is asymptotically efficient under mild conditions, and that this property can be lost if the intersample behavior of the input is not carefully accounted for in the SRIVC procedure.Moreover, we propose and analyze the statistical properties of an extension of SRIVC that is able to deal with input signals that cannot be interpolated exactly via hold reconstructions. The proposed estimator is generically consistent for any input reconstructed using zero or first-order-hold devices, and we show that it is generically consistent for continuous-time multisine inputs as well. Comparisons with the Maximum Likelihood technique and an analysis of the iterations of the method are provided, in order to reveal the influence of the intersample behavior of the output and to propose new robustifications to the SRIVC algorithm.
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| 38. |
- Gunnarsson, Svante, et al.
(author)
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Some asymptotic results in recursive identification using Laguerre models
- 1991
-
In: International Journal of Adaptive Control and Signal Processing. - : Wiley. - 0890-6327 .- 1099-1115. ; 5:5, s. 313-333
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Journal article (peer-reviewed)abstract
- This paper deals with recursive identification of time-varying systems using Laguerre models. Laguerre models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Three recursive algorithms are considered: the stochastic gradient algorithm, the recursive least squares algorithm and a Kalman-filter-like recursive identification algorithm. Simple and explicit expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly and that the model order is high. The derived expressions show how the use of Laguerre models affects the model quality with respect to tracking capability and disturbance rejection.
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| 39. |
- Gunnarsson, Svante, et al.
(author)
-
Some asymptotic results in recursive identification using Laguerre models
- 1990
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In: Proceedings of the IEEE Conference on Decision and Control. - Honolulu, HI, USA : Linköping University. ; , s. 1068-1073
-
Conference paper (peer-reviewed)abstract
- Frequency domain expressions for the quality of recursively identified Laguerre models are presented. These models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly, and that the model order is high. The model quality is evaluated by investigating the properties of the estimated transfer function, and explicit expressions for the mean square error (MSE) of the transfer function, and explicit expressions for the mean square error of the transfer function estimate are derived.
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| 40. |
- Gustafsson, Fredrik, et al.
(author)
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Blind Equalization by Direct Examination of the Input Sequences
- 1992
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In: Proceedings of the 1992 IEEE International Conference on Acoustics, Speech and Signal Processing. - 0780305329 ; , s. 701-704 vol.4
-
Conference paper (peer-reviewed)abstract
- The authors' approach to blind equalization examines the possible input sequences directly by using a bank of filters and, in contrast to common approaches, does not try to find an approximative inverse of the channel dynamics. The identifiability question of a noise-free finite impulse response (FIR) model is investigated. A sufficient condition for the input sequence (persistently exciting of a certain order) is given which guarantees that both the channel model and the input sequence can be determined exactly in finite time. A recursive algorithm is given for a time-varying infinite impulse response (IIR) channel model with additive noise, which does not require a training sequence. The estimated sequence is an arbitrarily good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. It shows fast convergence properties and good tracking ability.
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| 41. |
- Gustafsson, Fredrik, 1964-, et al.
(author)
-
Blind Equalization by Direct Examination of the Input Sequences
- 1995
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In: IEEE Transactions on Communications. - Linköping : Linköping University. - 0090-6778 .- 1558-0857. ; 43:7, s. 2213-2222
-
Reports (other academic/artistic)abstract
- This paper presents a novel approach to blind equalization (deconvolution), which is based on direct examination of possible input sequences. In contrast to many other approaches, it does not rely on a model of the approximative inverse of the channel dynamics. To start with, the blind equalization identifiability problem for a noise-free finite impulse response channel model is investigated. A necessary condition for the input, which is algorithm independent, for blind deconvolution is derived. This condition is expressed in an information measure of the input sequence. A sufficient condition for identifiability is also inferred, which imposes a constraint on the true channel dynamics. The analysis motivates a recursive algorithm where all permissible input sequences are examined. The exact solution is guaranteed to be found as soon as it is possible. An upper bound on the computational complexity of the algorithm is given. This algorithm is then generalized to cope with time-varying infinite impulse response channel models with additive noise. The estimated sequence is an arbitrary good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. The simulation results indicate fast convergence properties and good tracking abilities.
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| 42. |
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| 43. |
- Gustafsson, Fredrik, et al.
(author)
-
On simultaneous system and input sequence estimation
- 1993
-
In: IFAC Symposia Series. - Grenoble, Fr. ; , s. 11-16
-
Conference paper (peer-reviewed)abstract
- Equalization is concerned with estimation of the input sequence of a linear system given noisy measurements of the output signal. In case the system description is unknown we have the problem of blind equalization. A scheme for blind equalization which is based on the assumption that the input signal belongs to a finite alphabet is proposed. A finite impulse response model can be directly estimated by the least-squares method if the input sequence is known. Since we know that the number of possible input sequences is limited, we can associate one system estimate to each possible input sequence. This allows us to determine the a posteriori probability of an input sequence given output observations. The maximum a posteriori (MAP) input sequence estimate is then taken as the most probable input sequence. Sufficient conditions for identifiability of the input signal and the system are given. The complexity of this scheme increases exponentially with time. A recursive approximate MAP estimator of fixed complexity is obtained by, at each time update, only keeping the K most probable input sequences. This method is evaluated on a Rayleigh fading communication channel.
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| 44. |
- Gustafsson, Fredrik, et al.
(author)
-
On the Problem of Detection and Discrimination of Double Talk and Change in the Echo Path
- 1996
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In: Proceedings of the 1996 IEEE International Conference on Acoustics, Speech and Signal Processing. - 0780331923 ; , s. 2742-2745 vol.5
-
Conference paper (peer-reviewed)abstract
- The problem of detection and discrimination of double talk and change in the echo path in a telephone channel is considered. The phenomenon echo path change requires fast adaptation of the channel model to be able to equalize the echo dynamics. On the other hand, the adaption rate should be reduced when double talk occurs. Thus, it is critical to quickly detect a change in the echo path while not confusing it with double talk, which gives a similar effect. The proposed likelihood based approach compares a global channel model with a local one over a sliding window, both estimated with the recursive least squares algorithm.
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| 45. |
- Halvarsson, Susanne, et al.
(author)
-
Test statistics for low complexity change detection in dynamic systems based on averaged filter models
- 1996
-
In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - Atlanta, GA, USA : IEEE. - 0780331923 ; , s. 2845-2848
-
Conference paper (peer-reviewed)abstract
- This paper presents a new method to detect and discriminate between abrupt changes in dynamics and sudden changes in disturbance levels in dynamic systems. It is assumed that a normalized least mean square (NLMS) adaptive filter estimates the system. The detection method is based on the observation that the estimated taps behave differently in the two studied events. The convergence behavior of the taps is modeled using averaging theory, giving an exponential convergence behavior for each tap. Kalman filtering techniques, based on this model, are then used in order to design a new detection scheme.
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| 46. |
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| 47. |
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| 48. |
- Hansson, Anders, et al.
(author)
-
Continuous-time blind channel deconvolution using Laguerre shifts
- 2000
-
In: Mathematics of Control, Signals, and Systems. - 0932-4194 .- 1435-568X. ; 13:4, s. 333-346
-
Journal article (peer-reviewed)abstract
- The objective of this paper is to study the problem of continuous-time blind deconvolution of a pulse amplitude modulated signal propagated over an unknown channel and perturbed by additive noise. The main idea is to use so-called Laguerre filters to estimate a continuous-time model of the channel. Laguerre-filter-based models can be viewed as an extension of finite-impulse-response (FIR) models to the continuous-time case, and lead to compact and parsimonious linear-in-the-parameters models. Given an estimate of the channel, different symbol estimation techniques are possible. Here, the shift property of Laguerre filters is used to derive a minimum mean square error estimator to recover the transmitted symbols. This is done in a way that closely resembles recent FIR-based schemes for the corresponding discrete-time case. The advantage of this concept is that physical a priori information can be incorporated in the model structure, like the transmitter pulse shape.
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| 49. |
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| 50. |
- Heuberger, Peter S.C., et al.
(author)
-
Modelling and Identification with Rational Orthogonal Basis Functions
- 2005. - 1
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Book (peer-reviewed)abstract
- Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:Construction and analysis of generalized orthogonal basis function model structure;System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;System identification in the frequency domain;Design issues and optimal basis selection;Transformation and realization theory.Modelling and Identification with Rational Orthogonal Basis Functions affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.
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