288 research outputs found

    State observability and observers of linear-time-invariant systems under irregular sampling and sensor limittations

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    Abstract-State observability and observer designs are investigated for linear-time-invariant systems in continuous time when the outputs are measured only at a set of irregular sampling time sequences. The problem is primarily motivated by systems with limited sensor information in which sensor switching generates irregular sampling sequences. State observability may be lost and the traditional observers may fail in general, even if the system has a full-rank observability matrix. It demonstrates that if the original system is observable, the irregularly sampled system will be observable if the sampling density is higher than some critical frequency, independent of the actual time sequences. This result extends Shannon's sampling theorem for signal reconstruction under periodic sampling to system observability under arbitrary sampling sequences. State observers and recursive algorithms are developed whose convergence properties are derived under potentially dependent measurement noises. Persistent excitation conditions are validated by designing sampling time sequences. By generating suitable switching time sequences, the designed state observers are shown to be convergent in mean square, with probability one, and with exponential convergence rates. Schemes for generating desired sampling sequences are summarized

    Classification Algorithms based on Generalized Polynomial Chaos

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    Classification is one of the most important tasks in process system engineering. Since most of the classification algorithms are generally based on mathematical models, they inseparably involve the quantification and propagation of model uncertainty onto the variables used for classification. Such uncertainty may originate from either a lack of knowledge of the underlying process or from the intrinsic time varying phenomena such as unmeasured disturbances and noise. Often, model uncertainty has been modeled in a probabilistic way and Monte Carlo (MC) type sampling methods have been the method of choice for quantifying the effects of uncertainty. However, MC methods may be computationally prohibitive especially for nonlinear complex systems and systems involving many variables. Alternatively, stochastic spectral methods such as the generalized polynomial chaos (gPC) expansion have emerged as a promising technique that can be used for uncertainty quantification and propagation. Such methods can approximate the stochastic variables by a truncated gPC series where the coefficients of these series can be calculated by Galerkin projection with the mathematical models describing the process. Following these steps, the gPC expansion based methods can converge much faster to a solution than MC type sampling based methods. Using the gPC based uncertainty quantification and propagation method, this current project focuses on the following three problems: (i) fault detection and diagnosis (FDD) in the presence of stochastic faults entering the system; (ii) simultaneous optimal tuning of a FDD algorithm and a feedback controller to enhance the detectability of faults while mitigating the closed loop process variability; (iii) classification of apoptotic cells versus normal cells using morphological features identified from a stochastic image segmentation algorithm in combination with machine learning techniques. The algorithms developed in this work are shown to be highly efficient in terms of computational time, improved fault diagnosis and accurate classification of apoptotic versus normal cells

    Data driven techniques for modal decomposition and reduced-order modelling of fluids

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    In this thesis, a number of data-driven techniques are proposed for the analysis and extraction of reduced-order models of fluid flows. Throughout the thesis, there has been an emphasis on the practicality and interpretability of data-driven feature-extraction techniques to aid practitioners in flow-control and estimation. The first contribution uses a graph theoretic approach to analyse the similarity of modes extracted using data-driven modal decomposition algorithms to give a more intuitive understanding of the degrees of freedom in the underlying system. The method extracts clusters of spatially and spectrally similar modes by post-processing the modes extracted using DMD and its variants. The second contribution proposes a method for extracting coherent structures, using snapshots of high dimensional measurements, that can be mapped to a low dimensional output of the system. The importance of finding such coherent structures is that in the context of active flow control and estimation, the practitioner often has to rely on a limited number of measurable outputs to estimate the state of the flow. Therefore, ensuring that the extracted flow features can be mapped to the measured outputs of the system can be beneficial for estimating the state of the flow. The third contribution concentrates on using neural networks for exploiting the nonlinear relationships amongst linearly extracted modal time series to find a reduced order state, which can then be used for modelling the dynamics of the flow. The method utilises recurrent neural networks to find an encoding of a high dimensional set of modal time series, and fully connected neural networks to find a mapping between the encoded state and the physically interpretable modal coefficients. As a result of this architecture, the significantly reduced-order representation maintains an automatically extracted relationship to a higher-dimensional, interpretable state.Open Acces

    Modelling and Adaptive Control; Proceedings of an IIASA Conference, Sopron, Hungary, July 1986

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    One of the main purposes of the workshop on Modelling and Adaptive Control at Sopron, Hungary, was to give an overview of both traditional and recent approaches to the twin theories of modelling and control which ultimately must incorporate some degree of uncertainty. The broad spectrum of processes for which solutions of some of these problems were proposed was itself a testament to the vitality of research on these fundamental issues. In particular, these proceedings contain new methods for the modelling and control of discrete event systems, linear systems, nonlinear dynamics and stochastic processes

    The development and evaluation of computer vision algorithms for the control of an autonomous horticultural vehicle

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    Economic and environmental pressures have led to a demand for reduced chemical use in crop production. In response to this, precision agriculture techniques have been developed that aim to increase the efficiency of farming operations by more targeted application of chemical treatment. The concept of plant scale husbandry (PSH) has emerged as the logical extreme of precision techniques, where crop and weed plants are treated on an individual basis. To investigate the feasibility of PSH, an autonomous horticultural vehicle has been developed at the Silsoe Research Institute. This thesis describes the development of computer vision algorithms for the experimental vehicle which aim to aid navigation in the field and also allow differential treatment of crop and weed. The algorithm, based upon an extended Kalman filter, exploits the semi-structured nature of the field environment in which the vehicle operates, namely the grid pattern formed by the crop planting. By tracking this grid pattern in the images captured by the vehicles camera as it traverses the field, it is possible to extract information to aid vehicle navigation, such as bearing and offset from the grid of plants. The grid structure can also act as a cue for crop/weed discrimination on the basis of plant position on the ground plane. In addition to tracking the grid pattern, the Kalman filter also estimates the mean distances between the rows of lines and plants in the grid, to cater for variations in the planting procedure. Experiments are described which test the localisation accuracy of the algorithms in offline trials with data captured from the vehicle's camera, and on-line in both a simplified testbed environment and the field. It is found that the algorithms allow safe navigation along the rows of crop. Further experiments demonstrate the crop/weed discrimination performance of the algorithm, both off-line and on-line in a crop treatment experiment performed in the field where all of the crop plants are correctly targeted and no weeds are mistakenly treated
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