The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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This research is aimed to the development of a dynamic control to enhance the performance of the existing dynamic controllers for mobile robots. System dynamics of the car-like robot with nonholonomic constraints were...
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This research is aimed to the development of a dynamic control to enhance the performance of the existing dynamic controllers for mobile robots. System dynamics of the car-like robot with nonholonomic constraints were employed. A Backstepping approach for the design of discontinuous state feedback controller is used for the design of the controller. It is shown that the origin of the closed loop system can be made stable in the sense of Lyapunov. The control design is made on the basis of a suitable Lyapunov function candidate. The effectiveness of the proposed approach is tested through simulation on a car-like vehicle mobile robot.
The standard Capon beamformer (SCB) has better resolution and much better interference rejection capability than the data-independent beamformer provided that the array steering vector corresponding to the signal-of-i...
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The standard Capon beamformer (SCB) has better resolution and much better interference rejection capability than the data-independent beamformer provided that the array steering vector corresponding to the signal-of-interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the data-independent beamformer. Most of the early suggested robust adaptive methods are rather ad hoc in that the choice of their parameters are not directly related to the uncertainly of the steering vector. In this paper we provide a review of the recently proposed robust Capon beam-former (RCB) and doubly constrained robust Capon beamformer (DCRCB), which directly address the uncertainty of the steering vector and naturally extend the covariance fitting formulation of SCB to the case of uncertain steering vectors by enforcing a double constraint on the steering vector, viz. a constant norm constraint and an uncertainty set constraint. We also present several extensions and applications of RCB including constant-powerwidth RCB (CPRCB) and constant-beamwidth RCB (CBRCB) for acoustic imaging, rank-deficient robust Capon filter-bank (RCF) approach for spectral estimation, and rank-deficient RCB for landmine detection using forward-looking ground penetrating radar (FLGPR) imaging systems. The excellent performances of RCB, DCRCB, and the various extensions of RCB are demonstrated by simulated and experimental examples.
The maximum likelihood detector (MLD) for single differential modulation is easy to derive. On the other hand, the MLD problem associated with double differential modulation is much more complicated and solving it was...
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This paper discusses the safety assurance, capability features, and user-friendliness of Internet-based telerobotics, in the sense that how these features could be incorporated in order to enhance teleoperator perform...
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This paper discusses the safety assurance, capability features, and user-friendliness of Internet-based telerobotics, in the sense that how these features could be incorporated in order to enhance teleoperator performance. A comprehensive set of safety features are incorporated to the interface to screen and accept only the safe commands from the remote operator while discarding risky commands. A laser pointer is used to help remote operator in perceiving self-location and navigation, whereas orientation control has been completely automated and synchronized to the position commands of the teleoperator. The effectiveness of these features has been demonstrated by playing telerobotic mini-golf.
Experimental validation of a two-stage method for the identification of physical system parameters from experimental data is presented. The first stage compresses the data as an empirical model and the second stage th...
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Experimental validation of a two-stage method for the identification of physical system parameters from experimental data is presented. The first stage compresses the data as an empirical model and the second stage then uses data extracted from the empirical model of the first stage within a non-linear estimation scheme to estimate the unknown physical parameters. The approach handles unstable systems using exponential weighting.
This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identifi...
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This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identification parametric techniques are used in order to obtain a representative model for the controller design. With a suitable model, controllers of proportional integral type are designed, based on the root locus and fuzzy systems strategies, in order to improve the voltage regulation of the microgenerator system.
Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance th...
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Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance theory (ART), have shown promising results in this task. Most of the existing neural network based document clustering techniques rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features applied to our proposed document clustering technique, called hierarchical SOMART (HSOMART), which is a hierarchical network built up from independent SOM and ART neural networks. We describe a phrase grammar extraction technique, and the proposed HSOMART. The experimental results of clustering documents from the REUTERS corpus using the extracted phrases as features show an improvement in the clustering performance evaluated using the entropy and F-measure.
The use of multi-user multiple input multiple output (MIMO) processing algorithms in wireless communication systems requires new channel allocation algorithms that can intelligently assign users to channels that can b...
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The use of multi-user multiple input multiple output (MIMO) processing algorithms in wireless communication systems requires new channel allocation algorithms that can intelligently assign users to channels that can best take advantage of the spatial processing available at both transmitter and receiver. The availability of spatial processing at the receiver adds yet another variable to the classic channel allocation problem, making it very difficult to find the optimal solution for a particular set of users at a reasonable computational cost. We propose a two-step heuristic solution. The first step is the computation of a metric that quantifies the spatial compatibility of two users. The second step is to group the users into shared channels based on optimizing the sum of the compatibility metrics over all groups. we also propose a modified version of the algorithm in which the sub-channels (from different multipath components) of a single user are not required to share the same time-domain channel. In simulations, these algorithms come reasonably close to the optimal solution at a moderate computational cost.
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