| 1. |
- Abdalmoaty, Mohamed, 1986-, et al.
(författare)
-
The Gaussian MLE versus the Optimally weighted LSE
- 2020
-
Ingår i: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 37:6, s. 195-199
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 2. |
- Altafini, Claudio, et al.
(författare)
-
A feedback control scheme for reversing a truck and trailer vehicle
- 2001
-
Ingår i: IEEE transactions on robotics and automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1042-296X. ; 17:6, s. 915-922
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 3. |
- Andersson, Olov, 1979-, et al.
(författare)
-
WARA-PS : a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation
- 2021
-
Ingår i: Autonomous Intelligent Systems. - : Springer Nature. - 2730-616X. ; 1:1
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 4. |
- Andersson, Sören, et al.
(författare)
-
An adaptive array for mobile communication systems
- 1991
-
Ingår i: 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
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 5. |
|
|
| 6. |
- Avventi, Enrico, et al.
(författare)
-
Graphical Models of Autoregressive Moving-Average Processes
- 2010
-
Ingår i: The 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010).
-
Konferensbidrag (refereegranskat)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.
|
|
| 7. |
|
|
| 8. |
- Barenthin, Märta, et al.
(författare)
-
Data-driven methods for L2-gain estimation
- 2009
-
Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 1597-1602
-
Konferensbidrag (refereegranskat)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.
|
|
| 9. |
- Barenthin, Märta, et al.
(författare)
-
Gain estimation for Hammerstein systems
- 2006
-
Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 784-789
-
Konferensbidrag (refereegranskat)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.
|
|
| 10. |
- Barenthin, Märta, et al.
(författare)
-
Validation of stability for an induction machine drive using power iterations
- 2005
-
Ingår i: Proceedings of the 16th IFAC World Congress, 2005. - Prague. - 9783902661753 ; , s. 892-897
-
Konferensbidrag (refereegranskat)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.
|
|
