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.
In analyzing the behavior of refinery heaters, it turns out that the process dynamics is slow and can be cast into the form of input-output discrete-time controlled autoregressive and integrated moving-average (CARIMA...
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In analyzing the behavior of refinery heaters, it turns out that the process dynamics is slow and can be cast into the form of input-output discrete-time controlled autoregressive and integrated moving-average (CARIMA) model with large time-lag which poses difficulties in control design. To overcome these difficulties, modified Diophantine equations are introduced in this paper to facilitate the development of filtered outlet temperature predictions so that the closed-loop system (the refinery heater model with a recursive predictive controller) have desired stability properties. Simulation studies on a pilot-scale process heaters have illustrated the flexibility and computational effectiveness of the developed predictive control algorithm.
This paper presents significant new results on the stability and convergence properties of a general class of iterative learning control schemes derived using two-dimensional(2D) systems theory. These results apply fo...
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This paper presents significant new results on the stability and convergence properties of a general class of iterative learning control schemes derived using two-dimensional(2D) systems theory. These results apply for a general learning law which (explicitly) uses information from previous iterations or trials. A key feature of these results is that they are expressed in terms of standard linear systems theoretic properties, such as relative degree and the location of the zeros.
Repetitive, or multipass, processes are uniquely characterized by a series of sweeps, or passes through a set of dynamics defined over the so-called pass length which is finite and constant. The unique systems theoret...
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Repetitive, or multipass, processes are uniquely characterized by a series of sweeps, or passes through a set of dynamics defined over the so-called pass length which is finite and constant. The unique systems theoretic/control problem is that the sequence of outputs, or pass profiles, can contain oscillations which increase in amplitutde in the pass to pass direction. These processes can be modelled as a class of quarter plane causal 2D linear systems and this paper shows that the boundary (or pass initial) conditions alone can destabilize them. Hence they must be 'adequately modelled' in a given application and it is the boundary conditions which essentially distinguish the dynamic behaviour of linear repetitive processes from other classes of 2D linear systems.
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.
Many problems in nonlinear control theory (like, for instance, feedback linearization problem) leed to examination of integrability of a Pfaffian system. In a generic case a Pfaffian system is not integrable. Therefor...
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Many problems in nonlinear control theory (like, for instance, feedback linearization problem) leed to examination of integrability of a Pfaffian system. In a generic case a Pfaffian system is not integrable. Therefore, how to approximate non-integrable Pfafian systems by integrable ones and how this approximation can applied in practice appears to be a natural and important problem. In the present paper we establish some measures of non-integrability of Pfaffian system of arbitrary dimension and discuss their relation to approximations of non-integrable Pfaffian systems by integrable ones. Our work is motivated by expected applications to approximate feedback linearization of multi-input nonlinear systems.
Utilities are required to provide reliable power to customers. In the design stages, utilities need to plan ahead for anticipated future load growth under different possible scenarios. Their decisions and designs can ...
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ISBN:
(纸本)0780335228
Utilities are required to provide reliable power to customers. In the design stages, utilities need to plan ahead for anticipated future load growth under different possible scenarios. Their decisions and designs can affect the gain or loss of millions of dollars for their companies as well as customer satisfaction and future economic growth in their territory. This paper proposes and describes the general methodology to use fuzzy logic to fuse the available information for spatial load forecasting. The proposed scheme can provide distribution planners other alternatives to aggregate their information for spatial load forecasting.
Short-range scatterometer systems are used to obtain radar backscatter signatures for understanding the interaction between electromagnetic energy and geophysical media in a number of remote sensing applications. Unli...
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Short-range scatterometer systems are used to obtain radar backscatter signatures for understanding the interaction between electromagnetic energy and geophysical media in a number of remote sensing applications. Unlike long-range and intermediate-range radar systems, the sensitivity of short-range radars is not limited by thermal noise, but rather by reflections and leakage signals from the antenna and RF section. These leakage signals and their sidelobes are the primary sources of unwanted signals (coherent noise) in short-range FM radar systems. The authors have employed coherent noise reduction techniques to reduce the effects of these unwanted signal sources. Reduction of these coherent noise sources is critical to obtaining accurate backscatter measurements from geophysical targets. Recent advances include the use of phase correction to overcome limitations due to drift of local oscillators and effects of temperature changes on the system. The authors present results from the standard and phase-corrected coherent noise reduction techniques. These techniques increased the dynamic range of field and laboratory measurements of radar backscatter from sea ice and allows the authors to use data from past experiments that had previously been discarded due to low signal-to-noise ratio.
Computing the trajectories generated by an arbitrary system or process is extremely important for its analysis, especially for control and stability investigations. This paper analyses the fundamental matrix sequence ...
Computing the trajectories generated by an arbitrary system or process is extremely important for its analysis, especially for control and stability investigations. This paper analyses the fundamental matrix sequence (a discrete counterpart of the transition matrix in the continous case) for a linear unit memory repetitive process. The main result refers to the representation of the repetitive process in terms of the general singular Kurek model.
This paper describes a learning augmented recursive estimation approach for nonlinear dynamical systems having unmodeled nonlinearities. Utilizing a passive spatially-localized learning system, an approximation of the...
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This paper describes a learning augmented recursive estimation approach for nonlinear dynamical systems having unmodeled nonlinearities. Utilizing a passive spatially-localized learning system, an approximation of the unknown nonlinearity is synthesized online, based on state and parameter estimates from a nonlinear recursive estimator (an adaptive form of the extended Kalman filter). The learned model of the nonlinearity is used, in turn, to improve the performance of the recursive estimator. We demonstrate the approach on a second-order, mass-spring-damper system, where the spring stiffness is a nonlinear function of position. Simulation results indicate that, relative to more traditional adaptive estimation schemes, markedly improved estimation performance can be achieved.
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