Iterative Learning control (ILC) is now well established for linear and nonlinear dynamics in terms of both the underlying theory and experimental application. This approach is specifically targeted at applications wh...
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Iterative Learning control (ILC) is now well established for linear and nonlinear dynamics in terms of both the underlying theory and experimental application. This approach is specifically targeted at applications where the same operation is repeated over a finite duration with resetting between successive executions. Each execution is known as a trial and the novel principle behind ILC is to suitably use information from previous trials in the selection of the current trial input with the objective of sequentially improving performance from trial-to-trial. In this paper, new results on the extension of the ILC approach to the class of 2D systems that arise from certain methods of discretization of partial differential equations, resulting in the need to use a spatio-temporal setting for analysis. The resulting control laws can be computed using Linear Matrix Inequalities (LMIs). An illustrative example is also given and areas for further research discussed.
The problem of determining optimal observation strategies for identification of unknown parameters in distributed systems is discussed. Particularly, a setting where the measurement process is performed by collecting ...
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This study explores electroencephalographic (EEG) dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses. Arousing auditory feedback w...
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This study explores electroencephalographic (EEG) dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events during a sustained-attention driving task, which immediately agitated subject's responses to the events. The improved behavioral performance was accompanied by concurrent power suppression in the theta- and alpha-bands in the lateral occipital cortices. This study further explores the feasibility of estimating the efficacy of arousing feedback presented to the drowsy subjects by monitoring the changes in EEG power spectra.
Telecommunications networks are facing an increasing demand for Internet services. Therefore, a problem of network dimensioning with elastic traffic arises, which requires the allocation of bandwidth to maximize servi...
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A method is developed to solve an optimal node activation problem in sensor networks whose measurements are supposed to be used to estimate unknown parameters of the underlying process model in the form of a partial d...
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The paper discusses an approach to configure a sensor network in a spatial domain that will be used to identify unknown parameters of a distributed system. Particularly, given a finite number of possible sites at whic...
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This paper is concerned with RBF neural multi-models and a computationally efficient nonlinear Model Predictive control (MPC) algorithm based on such models. The multi-model has an ability to calculate predictions ove...
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The work presented in this paper deals with a fault tolerant control system designed for a boiler unit. The main core of the proposed system is the so-called on-line fault approximator built using locally recurrent ne...
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Two important problems in supervisory control are: constraint handling and economic optimisation, especially, if unconstrained direct feedback controllers (e.g. PIDs) are used. The new solution proposed in the paper i...
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This paper discusses a Model Predictive control (MPC) structure for economic optimisation of nonlinear technological processes. It contains two parts: an MPC economic optimiser/constraint governor and an unconstrained...
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