The Development Framework provides a highly automatic translation from a specification to an implementation. The specification is in a popular, graphical controlengineering notation typically representing a system wi...
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The Development Framework provides a highly automatic translation from a specification to an implementation. The specification is in a popular, graphical controlengineering notation typically representing a system with stringent reliability requirements and hard real time constraints. An interface has been constructed between the Development Framework and the commercially available dependability modelling tool, SURF-2. This tool is designed to support an evaluation based design approach. Multiple design solutions can be compared to assess the implications of design decisions on the dependability of the system under development. The software demonstration will show how the interface between the Development Framework and SURF-2 is used to model the inclusion of selected fault tolerant mechanisms in the system under development.< >
In various adaptive estimation applications, the unknown channel is characterized by an impulse response which consists of extended regions of negligible response or 'inactivity'. We consider the LMS estimatio...
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In various adaptive estimation applications, the unknown channel is characterized by an impulse response which consists of extended regions of negligible response or 'inactivity'. We consider the LMS estimation of a channel that may be well approximated by an FIR (filter) model with only a few nonzero tap coefficients within a given delay horizon or tap length n. When the number of nonzero tap coefficients m is small compared to the delay horizon n, the performance of the LMS estimator is greatly enhanced when this specific structure is exploited. We propose a consistent algorithm that performs identification of nonzero taps only.
We study genetic algorithms (GAs)-based identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network models and radial basis function network models. ...
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We study genetic algorithms (GAs)-based identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network models and radial basis function network models. Under perfect state measurements, we first show that a standard GA-based estimation scheme, in its full potential, even though leading to a satisfactory model for state estimation, may generally not yield a sufficiently accurate system model, i.e. the parameter estimates do not secure a good approximant for the system nonlinearity. We then introduce a new approach that utilizes a robust identification scheme, which leads to a good approximation of the nonlinearity in the system. Several numerical and simulation studies included in the paper demonstrate the effective use of GAs in this framework, in searching for the parameter values that lead to the "best" finite-dimensional approximation of the nonlinearities in the system dynamics.
In this paper, the authors describe their NSF sponsored research-curriculum program devoted to the topic of modeling and control of semiconductor manufacturing. The paper is focused for the most part on the curriculum...
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In this paper, the authors describe their NSF sponsored research-curriculum program devoted to the topic of modeling and control of semiconductor manufacturing. The paper is focused for the most part on the curriculum development under this program.
The paper addresses the worst-case parameter identification problem for linear systems under partial state measurements. Using the cost-to-come function method, worst-case identifiers are derived for SISO systems. The...
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The paper addresses the worst-case parameter identification problem for linear systems under partial state measurements. Using the cost-to-come function method, worst-case identifiers are derived for SISO systems. The worst-case identifier thus obtained includes the Kreisselmeier observer as part of its structure, with parameters set at some optimal values. Its structure is different from the common least-squares (LS) identifier, however, in the sense that there is an additional dynamics for the state estimate, coupled with the dynamics of the parameter estimate in a nontrivial way. A reduced-order identifier is obtained, which is numerically much better conditioned when the disturbances in the measurement equations are "small".
Studies the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. The difficulty associate...
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Studies the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. The difficulty associated with the persistency of excitation condition (inherent to the standard schemes in the neural identification literature) is circumvented here by a novel formulation and by using a new class of identification algorithms. By embedding the original problem in one with noise-perturbed state measurements, the authors present a class of identifiers (under /spl Lscr//sub 1/ and /spl Lscr//sub 2/ cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. For one special network structure, viz. the RBF network, the authors present a neural network version of an H/sup /spl infin//-based identification algorithm, and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information and noise-perturbed full-state information, it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the system nonlinearity.
The Digital Wave Processor, DWP [1-10], is an example of a dependable distributed system which has been built and will be flown on the ESA/NASA Cluster satellites. Pre-flight testing of the DWP instruments is nearly c...
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A control scheme is designed to minimize emissions and respond to rapid throttle changes in a fuel injected, spark ignition engine equipped with variable cam timing. The model is derived from engine mapping data for a...
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A control scheme is designed to minimize emissions and respond to rapid throttle changes in a fuel injected, spark ignition engine equipped with variable cam timing. The model is derived from engine mapping data for an eight cylinder experimental engine mounted in a dynamometer test cell; it is fundamentally a nonlinear and multivariable model. The control scheme jointly manages fuel and cam position.
A modular controller structure for automotive powertrains has certain benefits. These include improved productivity through module reuse, seamless integration of new features, transparent removal of obsolete features,...
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A modular controller structure for automotive powertrains has certain benefits. These include improved productivity through module reuse, seamless integration of new features, transparent removal of obsolete features, and module sharing across powertrain platforms. Modular architecture also potentially reduces the complexity in the design and calibration process, in that controller modules for different subsystems are developed independently. Due to the fact that the automotive powertrain system contains many highly interactive sub-systems, it is not clear that a modular controller development process can yield acceptable feedback controller performance with respect to emissions, fuel economy, and drivability. In this paper, we describe the engineering design issues associated with a decentralized development process, and the impact that the resulting decentralized controller has upon the dynamic response of the feedback system. We describe the possible detrimental consequences of subsystem interaction, and the potential of coordinated, multivariable feedback for alleviating these limitations. control of a spark ignition engine incorporating variable camshaft timing is used as a case study.
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