This paper presents an investigation into the utilisation of digital signal processing and parallel processing techniques for the real-time simulation of a flexible manipulator system. A finite dimensional simulation ...
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This paper presents an investigation into the utilisation of digital signal processing and parallel processing techniques for the real-time simulation of a flexible manipulator system. A finite dimensional simulation of the system is developed using a finite difference approximation to the governing dynamic equation of the manipulator. The proposed algorithm allows dynamic modification of the boundary conditions and the inclusion of a distributed actuator and sensor term in the system dynamic equation. The algorithm developed is implemented on a number of uni-processor and multi-processor, homogeneous and heterogeneous parallel architectures. The partitioning and mapping of the algorithm on the homogeneous and heterogeneous architectures is also explored. A comparison of the results of these implementations is made and discussed to establish merits of design and real-time processing requirements in the control of flexible manipulator systems. (C) 1996 Academic Press Limited
This paper describes work in progress on two extensions to the environment of a Development Framework for the design of control system software; hybrid system design and design of dependable systems. The Development F...
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This paper describes work in progress on two extensions to the environment of a Development Framework for the design of control system software; hybrid system design and design of dependable systems. The Development Framework automatically converts a controlengineering specification into a parallel implementation. Hybrid control systems are a combination of real-time control law and discrete-state logic. The Framework can be adapted so that it may handle discrete events such as mode switching. A statechart tool, Statemate [i-Logix95], is used to specify and model discrete-state components. The design phase of the Development Framework supports the integration of the two sets of specification. Initial work to support the design of dependable systems has included the implementation of an interface between the Framework design model and the dependability modelling tool, SURF-2. Stochastic Petri net models of selected fault-tolerant mechanisms are currently supported. The models enable sensitivity analysis and comparison of competing architectures.
We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behav...
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We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems engineering and automaticcontrol Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm.
This paper presents an investigation into tne nature ot control algorithms in relation to that of the architectures for real-time applications so that a close match be forged between them. Several algorithms of regula...
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This paper presents an investigation into tne nature ot control algorithms in relation to that of the architectures for real-time applications so that a close match be forged between them. Several algorithms of regular and irregular nature are implemented on a number of architectures possessing different features. Partitioning and mapping of the algorithms, algorithm regularity and hardware and algorithm granularity are addressed in implementing the algorithms on parallel architectures. A comparison of the results of implementations is made to establish merits of the development of parallel architectures for real-time applications. Finally, the potential of heterogeneous architectures in real-time applications has been justified by the development of a three-node architecture and its performance verification in comparison to uni-processor and two-node homogeneous and heterogeneous architectures.
This paper presents a robust fault detection system (FDS) for dynamic systems with unmodeled dynamics. In the FDS, umnodeled dynamics is first qualified as soft bound, which as well as model parameters are estimated u...
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This paper presents a robust fault detection system (FDS) for dynamic systems with unmodeled dynamics. In the FDS, umnodeled dynamics is first qualified as soft bound, which as well as model parameters are estimated using a robust identification algorithm. Then as a fault detection index, Kullback discrimination information (KDI) is derived into a feasible form and an index of umnodeled dynamics is also introduced. A decision making scheme is thus developed so that fault detection is carried out based on the KDI, the index of umnodeled dynamics and other prior information about the system.
In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to gener...
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In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary random image sequences. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Due to the reversibility of a reversible DTCNN, the same reversible DTCNN is used to recover the copyright label from a labelled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. computer simulation results are presented.
The aim of this work is the design of a class of neural networks for nonlinear function identification: the so-called neural modules. A neural module is a neural network with an internal structure specially designed t...
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The aim of this work is the design of a class of neural networks for nonlinear function identification: the so-called neural modules. A neural module is a neural network with an internal structure specially designed to be able to learn and mimic the behaviour of a certain class of dynamic systems. Neural networks are abstract models well suited for approximating nonlinear functions. The training cost and the structural complexity of neural networks can be drastically reduced if a-priori knowledge of the function to be learned is internally incorporated in the form of structural constraints. The resulting neural network has less parameters than a conventional one, much faster learning convergence and it can provide meaningful information about the learned nonlinear function. This paper describes the design of a useful set of neural modules for system identification and gives general guidelines for the design of neural modules. The resulting networks are evaluated and their use on general systems identification is pointed out.
In order to improve the resolution of spectrum analysis, the traditional method is to take more sampling periods, e. g. 50 sampling periods corresponding to the spectrum interval of I Hz. This method is not suitable t...
In order to improve the resolution of spectrum analysis, the traditional method is to take more sampling periods, e. g. 50 sampling periods corresponding to the spectrum interval of I Hz. This method is not suitable to the interharmonic measurement, because most interharmonics vary rather quickly with the time and so the analysis results based on long time sampling may be useless. In this paper the zero-padding technique with short sampling time is used to resolve interharmonics, and the zoom FFT is used to decrease the computation time in order to meet the needs of real-time measurement. The simulating computation shows that the resolution for two adjacent frequency components is usually sufficient to measure harmonics and interharmonics in power systems with acceptable short sampling and computation time. This method is also suited to analyse harmonics and interharmonics when there exists an undesirable asynchronous deviation between the sampling period and the signal period.
In this letter, we study the use of adaptive controllers to maintain the synchronization of two Chua's oscillators when the channel and circuit parameters are time-varying. We present both computer simulation resu...
In this letter, we study the use of adaptive controllers to maintain the synchronization of two Chua's oscillators when the channel and circuit parameters are time-varying. We present both computer simulation results and physical experimental results to verify the operation of the designs.
In this paper, global asymptotic stability of linear, time-variant, finite dimensional, zero input difference equations is investigated. We propose a technique that may be utilized to obtain regions of asymptotic stab...
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In this paper, global asymptotic stability of linear, time-variant, finite dimensional, zero input difference equations is investigated. We propose a technique that may be utilized to obtain regions of asymptotic stability in the coefficient space that incorporate information regarding the maximum rate of change of system parameters. Use of different matrix norms provide different ''shapes'' for the maximum allowable coefficient perturbations.
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