In this paper, we explore the possibility of applying Monte Carlo methods (i.e., randomization) to semi-infinite programming problems. Equivalent stochastic optimization problems are derived for a general class of sem...
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The object-oriented design methodology has been manipulated to model the standard CMOS fabrication technology. Detecting and characterizing the objects and classes within the basic object-oriented features of encapsul...
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The object-oriented design methodology has been manipulated to model the standard CMOS fabrication technology. Detecting and characterizing the objects and classes within the basic object-oriented features of encapsulation, inheritance and polymorphism, as well as discussing the model framework and model characterization has been the main pursuits of this works. This modeling approach puts forth the chance of a fully automated fabrication system, with well-characterized hardware and reusable software. This model is also a promising candidate for enhancing the system performance to the minimum level required for fully integrated agent-oriented entities, which can reconfigure themselves for future technology generations.
In this paper a new robust steepest-descent algorithm for discrete-time iterative learning control is introduced for plant models with multiplicative uncertainty. A theoretical analysis of the algorithm shows that if ...
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In this paper a new robust steepest-descent algorithm for discrete-time iterative learning control is introduced for plant models with multiplicative uncertainty. A theoretical analysis of the algorithm shows that if a tuning parameter in the algorithm is selected to be sufficiently large, the algorithm will result in monotonic convergence if the plant uncertainty satisfies a positivity condition. This is a major improvement when compared to the standard steepest-descent algorithm, which lacks a mechanism for finding a balance between convergence speed and robustness. Experimental work on a gantry robot is performed to demonstrate that the algorithm results in near perfect tracking in the limit.
A controlsystemsengineering approach, employing a two-level overall system architecture and different but compatible formalisms for system representation on the upper and lower levels, has been investigated in detai...
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This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the...
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The introduction of product quality self-regulation to food-cooking extrusion is an important aspect of process control within food manufacturing industries. In order to design an automatic control system for product ...
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The introduction of product quality self-regulation to food-cooking extrusion is an important aspect of process control within food manufacturing industries. In order to design an automatic control system for product quality, a mathematical model of the food extruder is required. As first-principles models are difficult to obtain in this context, a food extruder is a good candidate for applying system identification tools. This paper presents the application of continuous time system identification to such a food cooking extruder. More specifically, the reported application features an automated identification experiment apparatus designed using relay feedback control mechanisms and instrumented through existing real time supervisory system for the extruder. Experimental data from the food extruder are obtained and analysed using our identification approach.
Linear space-time block coding (STBC) is a conceptually simple transmission technique for channels with multiple transmit and receive antennas. In this paper, we study a subclass of linear STBC, namely orthogonal STBC...
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Linear space-time block coding (STBC) is a conceptually simple transmission technique for channels with multiple transmit and receive antennas. In this paper, we study a subclass of linear STBC, namely orthogonal STBC (OSTBC), with the primary goal of contributing towards a complete understanding of OSTBC. In particular, we prove that OSTBC is optimal in an important minimum mean-square error (MSE) sense. As a by-product, we also obtain a concise characterization of linear and orthogonal STBC's as well as the relationship between them. The MSE optimality of OSTBC shown herein can be used as a way of introducing this coding scheme in a formal manner.
The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known...
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The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we will first provide a complete analysis of a norm constrained Capon beamformer (NCCB), which uses a norm constraint on the weight vector to improve the robustness against array steering vector errors and noise. Our analysis is thorough and sheds more light on the choice of the norm constraint than what was commonly known. We also briefly discuss our recently proposed robust Capon beamformer (RCB), which is obtained via extending the covariance matrix fitting approach to the case of uncertain steering vectors. Our RCB is based on a clear theoretical background and explicitly addresses the steering vector uncertainty problems. Both NCCB and RCB can be efficiently computed at a comparable cost with that of SCB. Performance comparisons of NCCB and RCB via a number of numerical examples are also presented.
The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known...
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The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we provide a natural extension of SCB, obtained via covariance matrix fitting, to the case of uncertain steering vectors by enforcing a double constraint on the array steering vector, viz. a constant norm constraint and a spherical uncertainty set constraint, which we refer to as the doubly constrained robust Capon beamformer (DCRCB). DCRCB can be efficiently computed at a comparable cost with that of SCB. Performance comparisons of DCRCB and our previously proposed robust Capon beamformer (RCB) are also presented via a number of numerical examples.
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