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检索条件"机构=Control Methods and Robotics"

共 83 条记录，以下是41-50 订阅

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Static Output Feedback: An LMI Condition for Stabilizability Based on Passivity Indices

Static Output Feedback: An LMI Condition for Stabilizability...

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IEEE Multi-Conference on Systems and control

作者： Diego de S. Madeira Jurgen Adamy Control Methods and Robotics Lab Technische Universitat Darmstadt

ISBN: (纸本)9781509007561

This note deals with the static output feedback stabilization problem of linear time-invariant (LTI) systems, one of the most relevant open problems in the field of control theory. We present a new sufficient linear matrix inequality (LMI) condition to test stabilizability by static output feedback. The notion of passivity indices, as a measure of the level of passivity of a system, is employed. Unlike the methods currently available in the literature, our LMI condition is non iterative and does not rely on similarity transformations, allowing for a straightforward determination of a constant feedback gain that asymptotically stabilizes the closed-loop. We also provide numerical examples of our strategy in order to demonstrate its applicability in different scenarios.

关键词： Output feedback Linear systems Nonlinear systems Linear matrix inequalities Iterative methods Indexes

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Flow-Decoupled Normalized Reprojection Error for Visual Odometry

Flow-Decoupled Normalized Reprojection Error for Visual Odom...

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IEEE International Conference on Intelligent Transportation Systems

作者： Martin Buczko Volker Willert Control Methods and Robotics Lab TU Darmstadt Germany

ISBN: (纸本)9781509018901

In this paper, we present an iterative two-stage scheme for precise and robust frame-to-frame feature-based ego-motion estimation using stereo cameras. We analyze the characteristics of the optical flows and reprojection errors that are independently induced by each of the decoupled six degrees of freedom motion. As we will show, the different characteristics of these induced optical flows lead to a reprojection error that depends on the coordinates of the features. When using a proper normalization of the reprojection error, this coordinate-dependency can be almost completely removed for decoupled motions. Furthermore, we present a way to use these results for automotive application where rotation and forward motion arc coupled. This is done by compensating for the flow that is induced by the rotation, which decouples the translation flow from the overall flow. The resulting method generalizes the ROCC approach [4], where a robust outlier criterion was introduced and proved to increase robustness and quality for large forward translation motions. Therewith the proposed method generalizes ROCC to almost all possible automotive motions. The performance of the method is evaluated on Kitti benchmark and currently2 reaches the best translation error of all camera-based methods.

关键词： Adaptive optics Optical variables measurement Visualization Estimation Optical imaging Measurement uncertainty Feature extraction

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Asymptotic stabilization of nonlinear systems using passivity indices

Asymptotic stabilization of nonlinear systems using passivit...

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American control Conference

作者： Diego de S. Madeira Jurgen Adamy Control Methods and Robotics Lab Technische Universitat Darmstadt 64283 Germany

ISBN: (纸本)9781467386838

This work presents a new controller design technique for asymptotic stabilization of nonlinear systems using passivity indices. The aforementioned indices measure the level of passivity of a system and we address the question of how to use this information to design a dynamic output feedback controller that stabilizes the closed-loop at a given equilibrium. As full state information is not always available for feedback, a measurable output has to be used. The main contribution of this paper consists of a systematic way of determining a stabilizing controller based on the passivity indices of the plant and on sum of squares (SOS) relaxations. A new method for determining the passivity indices of a nonlinear system in a domain of its state is presented as well. An example is provided in order to demonstrate the applicability of our strategy. Polynomial input-affine systems are considered.

关键词： Nonlinear systems Asymptotic stability Output feedback Stability criteria Testing Indexes Nonlinear systems Output feedback Stability criteria Passivity asymptotic stability region Indexes

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Speaker adaptation for support vector machine basedword prominence detection 8

Speaker adaptation for support vector machine basedword prom...

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8th Speech Prosody 2016

作者： Schnall, Andrea Heckmann, Martin TU Darmstadt - Control Methods and Robotics Holzhofallee 38 Darmstadt64295 Germany Honda Research Institute Europe GmbH Carl-Legien-Str. 30 Offenbach/Main63073 Germany

In this paper we propose a new speaker adaptation method to improve the detection of prominent words in speech. Prosodic cues are difficult to extract, due to the different features different speakers are using to express, for example prominence. To overcome the problem of variation from the pool of speakers used during training and those encountered during deployment, in speech recognition speaker adaptation techniques like fMLLR turned out to be very useful. In the case of prominence detection, our results have shown that a discriminative classifier like SVM works better thanGMM. Existing adaptation methods like fMLLR are developed for GMM-HMM based classifiers under the assumption that the data has a Gaussian distribution. This does not hold for our data, using the fMLLR with the SVM leads not to an improvement for our problem area. Therefore we propose a new adaptation method, which adapts the data to the RBF kernel of the SVM, subsequently regularizing it with the fMLLR. We investigate how this method can be used to adapt a new speaker to a speaker independent model for word prominence detection. We show that the error rate improves from the speaker adaptation from 16.4% to 14.4%. © 2016, International Speech Communications Association. All rights reserved.

关键词： Speech recognition

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Balancing gaussianity and sparseness in feature-space speaker adaptation for word prominence detection

Balancing gaussianity and sparseness in feature-space speake...

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12. ITG-Fachtagung Sprachkommunikation - 12th ITG Conference on Speech Communication

作者： Schnall, Andrea Heckmann, Martin TU Darmstadt Control Methods and Robotics Holzhofallee 38 Darmstadt64295 Germany Honda Research Institute Europe GmbH Carl-Legien-Str. 30 Offenbach/Main63073 Germany

ISBN: (纸本)9783800742752

Word prominence is an essential part of communication, e.g. to express important information. But due to speaker variations, it is difficult to extract these cues. A common method therefore is the adaptation of data from a novel speaker to a model trained from a larger pool of speakers. For the detection via an SVM classifier we developed an adaptation method based on the radial basis function of the SVM with a Gaussian regularization, which is derived from fMLLR. With this method we could improve the word prominence detection, but, as it is a common problem, a sufficient amount of labeled data is necessary. In this paper we show that by adding an additional regularization term, we can further improve the classification and reduce the amount of necessary data notably. Additionally, we investigate how the weighting of the different terms influences the performance and can be used to improve the results. © 2016 VDE VERLAG GMBH.

关键词： Radial basis function networks

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Transmission cost-sharing in multi-supplier power grid

Transmission cost-sharing in multi-supplier power grid

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IEEE International Conference on Smart Energy Grid Engineering (SEGE)

作者： Ivan Popov Alexander Krylatov Victor Zakharov TU Darmstadt Control Methods and Robotics Lab Germany Saint Petersburg State University Russia

Ever-increasing energy consumption and growing penetration of renewable energy sources stimulate the development of new power grid models and architectures. Since the decentralization of power grids causes the unreliability of power supply, it is crucial to switch to a production-oriented consumption in order to provide the stability of a grid. Power flows in a grid are distributed according to Kirchhoff's laws and use multiple parallel lines in circuitous routes. Once the profile of energy injections and withdrawals is fixed, the values of load current flows can be defined over the network. Generally, it is not possible to directly determine the accordance between the energy contracts and the loop flows that contribute to the total congestion in network links. In this work, we describe a multi-supplier power grid model. Then we formulate a nonlinear minimization problem which solution gives a flow distribution fulfilling Kirchhoff's laws. Finally, we introduce a cost-sharing technique for finding individual transmission costs.

关键词： Contracts Kirchhoff's Law Power grids Transportation Generators Electric potential Optimization

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Probabilistic, Discriminative Maneuver Estimation in Generic Traffic Scenes using Pairwise Probability Coupling

Probabilistic, Discriminative Maneuver Estimation in Generic...

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IEEE International Conference on Intelligent Transportation Systems

作者： Stefan Klingelschmitt Volker Willert Julian Eggert Control Methods and Robotics Lab Technische Universitat Darmstadt Honda Research Institute Europe GmbH

ISBN: (纸本)9781509018901

Future advanced driver assistance systems as well as autonomous vehicles are expected to further increase their areas of applicability. Reliable maneuver estimations are a prerequisite for many of the intended functionalities. Accordingly, maneuver estimation systems need to cover a wide range of scenarios. The majority of recently presented approaches are targeted at fixed scenarios. However, having specialized maneuver estimation systems covering each possible scenario is unrealistic. Therefore, we present an approach for tackling discriminative maneuver estimations in generic traffic scenes. It is based on reusable, partial classifiers that are combined online using a technique called pairwise probability coupling. As a result we are able to make discriminative maneuver estimations in generic traffic scenes. The benefits and applicability are presented on inner-city real-world data sets. Our evaluation indicates that the assembled probabilistic maneuver estimation is not only able to outperform generative models;it surpasses the performance of specially designed models due to the reduced complexities of the partial classifiers.

关键词： Probability advanced driver assistance systems (ADAS) Traffic Autonomous Vehicles Pairwise Classifiers

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Generic Hypothesis Generation for Small and Distant Objects

Generic Hypothesis Generation for Small and Distant Objects

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IEEE International Conference on Intelligent Transportation Systems

作者： Ann-Katrin Batzer Christian Scharfenberger Michelle Karg Stefan Lueke Jurgen Adamy Control Methods and Robotics Technische Universitat Darmstadt Vision and Image Processing Lab University of Waterloo Continental AG

ISBN: (纸本)9781509018901

Existing approaches to object detection address the generation of object hypotheses by extracting several cues in natural and automotive images, relying on objects with sufficiently high resolution. Very little to almost no approaches, however, address the generation of hypothesis of very small or distant objects in images such as on motorways. Here, we propose a simple yet effective approach to generating hypotheses of small and distant objects in images. Our key contribution is a novel voting scheme that makes efficient use of the different appearance of small candidate objects to their environment. We model the environment as being composed of very few regions with homogeneous appearance, extracted by evaluating the inner statistics of an image in an unsupervised fashion. Small regions that can not be assigned to the environment form potential candidate locations. Experimental results on motorway scenes with cars, traffic signs, and other automotive objects based on a variety of performance evaluation metrics show that our approach provides promising results, and outperforms one of the currently leading approaches in generating hypotheses for small and/or distant objects in images.

关键词： Vehicles Robustness Image resolution Cameras Feature extraction Computational modeling Roads

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Receding-horizon planning using recursive Monte Carlo Tree Search with Sparse Action Sampling for continuous state and action spaces

Receding-horizon planning using recursive Monte Carlo Tree S...

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American control Conference

作者： Moritz Schneider Laboratory of Control Methods and Robotics Institute of Automatic Control and Mechatronics Dept. of Electrical Engineering and Information Technology Technische Universit?t Darmstadt Germany

ISBN: (纸本)9781467386838

This paper introduces a recursive, sampling-based Monte Carlo Tree Search (MCTS) approach to planning, i.e. receding horizon control, in continuous state and action nonlinear set-point control problems. Usually, predictive control methods for nonlinear systems aim at (sub-)optimal control behavior through solving a dynamic optimization problem over a fixed prediction horizon in every time step. Tree-based methods, which have recently gained interest in planning, model-based reinforcement learning and path planning, replace the optimization problem by an incremental probabilistic search for (sub-)optimal open-loop control sequences in the space of variable-length closed-loop trajectories. The benefit of using a weaker optimization procedure is that the algorithm is very simple to understand/apply and works for general nonlinear systems. This article is concerned with increasing the control performance and sampling-efficiency of MCTS in continuous state and action spaces based on ideas from the field of standard nonlinear model predictive control.

关键词： Search problems Planning Aerospace electronics Optimization Trajectory Monte Carlo methods Prediction algorithms

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Comparing speaker independent and speaker adapted classification for word prominence detection

Comparing speaker independent and speaker adapted classifica...

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IEEE Spoken Language Technology Workshop

作者： Andrea Schnall Martin Heckmann Honda Research Institute Europe GmbH Offenbach/Main Germany TU Darmstadt - Control Methods and Robotics Darmstadt Germany

ISBN: (纸本)9781509049042

Prosodic cues are an important part of human communication. One of these cues is the word prominence which is used to e.g. highlight important information. Since individual speakers use different ways of expressing prominence, it is not easily extracted and incorporated in a dialog system. As a consequence, up to date prominence only plays a marginal role in human-machine communication. In this paper we compare DNNs and SVMs trained speaker independently with the results of classification with SVM using a speaker adaptation method we recently developed. This adaptation method is based on the radial basis function of the SVM with a Gaussian regularization, which is derived from fMLLR. With this adaptation, we can notably reduce the problem of speaker variations. We present detailed evaluations of the methods and discuss advantages and shortcomings of the proposed approaches for word prominence detection.

关键词： Support vector machines Adaptation models Sparse matrices Data models Neural networks Speech recognition Feature extraction

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