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Dissertations / Theses on the topic 'Control systems/gas networks'
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Wong, Man Lam. "The application of constrained optimization gas transmission networks." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235972.
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Parkinson, J. S. "Control system design for low pressure gas distribution networks." Thesis, University of Manchester, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378367.
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Pearson, D. W. "Robust observer design and application to gas networks." Thesis, Coventry University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380696.
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Mu, Junxia. "Design and application of advanced control methods to gas turbines and networked systems." Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/design-and-application-of-advanced-control-methods-to-gas-turbines-and-networked-systems(c6311aed-db75-415a-8b2a-f1594479f685).html.
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This thesis deals with the design and application of modern control techniques to a Rolls Royce aircraft gas turbine engine and networked systems. It is motivated by the need to fully exploit recent advances in control engineering and investigate the suitability of various control methods to gas turbine engines and networked systems. The main contributions of the first part of the thesis relate to the gas turbine engine control. Due to the nonlinearities of the gas turbine engines, the rate limiter and saturation constraints on the fuel feed, the aim is to illustrate the potential of a global nonlinear controller to cover the engine operating range. Several nonlinear control methods, gain-scheduling PID controller, approximate model predictive control (AMPC) and nonlinear model predictive control (NMPC), are presented along with the corresponding control algorithms. Since the parameters in a gain-scheduling PID controller change with the operating range, the need is apparent for a global nonlinear controller to cover its operating range. AMPC and NMPC are then demonstrated to be capable of providing a global nonlinear controller for the engine and can be used in the place of the gain scheduling PID controller. It is shown that AMPC is more preferable than NMPC if computational time is at a premium. The main theme of the second part of the thesis is the design and application of the networked predictive control (NPC) to compensate for the network delay and data packet dropout in both forward and backward channels for networked systems. NPC using both modified model predictive control and generic polynomial method is presented along with the corresponding control algorithms. For both approaches, the system stability for a fixed network delay is presented and an analytical stability criterion is obtained. This provides some guidelines on how to choose the NPC parameters in the case of random network delay. The performance of NPC can be further improved by using a robust NPC (RNPC). To validate the performance using the proposed control methods, a servo motor system is then used for both Intranet and Internet based simulations and practical experiments. A networked control test rig along with the network delay measurement method is used for real-time implementation. It is shown that both NPC and RNPC can efficiently compensate for the network delay and data packet dropout in both channels. This thesis provides basis for the real-time implementation of advanced control methods in gas turbine engines. While this work was applied to a gas turbine engine, these techniques can be applied to a range of nonlinear control systems. The work on the networked predictive control presented in this thesis can provide basis for further research relating to this area.
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DORASKEVICIUS, JUNIOR WALDEMAR. "Identificacao de padrões em sistemas supervisórios de instalações de reatores nucleares e em sistemas de gasodutos utilizando mapas auto-organizáveis." reponame:Repositório Institucional do IPEN, 2005. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11273.
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Kleman, Björn, and Henrik Lindgren. "Evaluation of model-based fault diagnosis combining physical insights and neural networks applied to an exhaust gas treatment system case study." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176650.
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Fault diagnosis can be used to early detect faults in a technical system, which means that workshop service can be planned before a component is fully degraded. Fault diagnosis helps with avoiding downtime, accidents and can be used to reduce emissions for certain applications. Traditionally, however, diagnosis systems have been designed using ad hoc methods and a lot of system knowledge. Model-based diagnosis is a systematic way of designing diagnosis systems that is modular and offers high performance. A model-based diagnosis system can be designed by making use of mathematical models that are otherwise used for simulation and control applications. A downside of model-based diagnosis is the modeling effort needed when no accurate models are available, which can take a large amount of time. This has motivated the use of data-driven diagnosis. Data-driven methods do not require as much system knowledge and modeling effort though they require large amounts of data and data from faults that can be hard to gather. Hybrid fault diagnosis methods combining models and training data can take advantage of both approaches decreasing the amount of time needed for modeling and does not require data from faults. In this thesis work a combined data-driven and model-based fault diagnosis system has been developed and evaluated for the exhaust treatment system in a heavy-duty diesel engine truck. The diagnosis system combines physical insights and neural networks to detect and isolate faults for the exhaust treatment system. This diagnosis system is compared with another system developed during this thesis using only model-based methods. Experiments have been done by using data from a heavy-duty truck from Scania. The results show the effectiveness of both methods in an industrial setting. It is shown how model-based approaches can be used to improve diagnostic performance. The hybrid method is showed to be an efficient way of developing a diagnosis system. Some downsides are highlighted such as the performance of the system developed using data-driven and model-based methods depending on the quality of the training data. Future work regarding the modularity and transferability of the hybrid method can be done for further evaluation.
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Choudhary, P. A. "State estimation applied to gas distribution networks." Thesis, University of Manchester, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378007.
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The need to make more efficient use of energy resources by improved methods of control, the deferment of expensive pipework reinforcement or replacement and the requirement to accommodate different loading patterns on sections of the gas network have made the requirement for effective feedback control essential. Gas distribution systems are large and complex and a major problem in applying conventional feedback techniques is the cost and reliability of transmitting the data necessary for automatic control. In order to overcome these difficulties, state estimation techniques have been investigated as a means of providing information about a distribution system from a minimum number of measurement points. The theoretical background to the study is reviewed including the modelling technique and the results of experimental work which has been performed on a distribution network in order to verify the techniques developed are presented. The results show that state estimation techniques have considerable potential for this type of application.
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Ho, Chi-Jui. "Call admission control in cellular networks." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/15730.
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Ruano, Antonio Eduardo de Barras. "Applications of neural networks to control systems." Thesis, Bangor University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304917.
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Somanath, Amith. "Control of stochastically interacting systems on networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112459.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 109-117).<br>The goal of this thesis is to develop control theoretic analysis and algorithms for characterizing and controlling stochastically interacting systems on networks. Such systems have three essential features - (i) they are stochastic processes, (ii) they are made up several individual components connected through a network, and (iii) the connected components influence one another through local interactions. This thesis presents analysis and control of three representative examples of such systems from the fields of spreading processes, smart manufacturing, and transport phenomena. In the first part of the thesis, control of spreading processes on lattices is considered. Analysis and control of spreading processes is difficult because the dimensionality of state space is often large. A common approach to this issue is to use mean field approximations which completely average out the stochasticity inherent to these systems. Instead this thesis, using recently developed tools from nonequilibrium statistical physics, accurately characterizes open loop behavior of spreading processes in its stable, neutral and unstable regimes. Such a characterization is not possible using approximate models. Furthermore, for an unstable spreading process, a randomized control policy is proposed that is optimal in both resource allocation and control effort. In the second part of the thesis, control of smart manufacturing processes is considered. Due to increased product customization and rapidly changing demands, the recent trend in manufacturing is to shift towards modular architectures. Such a shift presents scheduling challenges in a rapidly and dynamically changing environment. This thesis presents a queuing theory framework for modeling job flow, and a stochastic scheduling algorithm. Such an approach is amenable for fast implementation while achieving balanced load among operating agents. In the last part of the thesis, control of transport phenomena is considered. Transport phenomena are systems that are in nonequilibrium. Even though study and analysis of systems exhibiting nonequilibrium phenomena have been considered in the past, there is no effective way to control or modify the behavior of these systems. This thesis presents control theoretic formulations for systems in nonequilibrium. Starting from a paradigmatic model for traffic flow known as totally asymmetric simple exclusion process (TASEP), thesis presents routing policies to achieve maximum flow rate of traffic for all set of input traffic conditions. Extensions are also made to TASEP models on intersections and generic road networks.<br>by Amith Somanath.<br>Ph. D.
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Ruano, Antonio. "Applications of neural networks to control systems." Doctoral thesis, University College of North Wales, 1992. http://hdl.handle.net/10400.1/2320.
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Tese de dout., Engenharia Electrónica, School of Electronic Engineering Science,
Univ. of Wales, Bangor, 1992<br>This work investigates the applicability of artificial neural networks to control systems. The following properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt.
Exploiting the first feature, a new method is proposed for PID autotuning.
Based on integral measures of the open or closed loop step response, multilayer perceptrons (MLPs) are used to supply PID parameter values to a standard PID controller. Before being used on-line, the MLPs are trained offline, to provide PID parameter values based on integral performance criteria.
Off-line simulations, where a plant with time-varying parameters and time varying transfer function is considered, show that well damped responses are obtained. The neural PID autotuner is subsequently implemented in real-time. Extensive experimentation confirms the good results obtained in the off-line simulations.
To reduce the training time incurred when using the error back-propagation algorithm, three possibilities are investigated. A comparative study of higherorder methods of optimization identifies the Levenberg-Marquardt (LM)algorithm as the best method. When used for function approximation purposes, the neurons in the output layer of the MLPs have a linear activation function.
Exploiting this linearity, the standard training criterion can be replaced by a
new, yet equivalent, criterion. Using the LM algorithm to minimize this new criterion, together with an alternative form of Jacobian matrix, a new learning algorithm is obtained. This algorithm is subsequently parallelized. Its main blocks of computation are identified, separately parallelized, and finally connected together. The training time of MLPs is reduced by a factor greater
than 70 executing the new learning algorithm on 7 Inmos transputers.
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Ozcaldiran, Kadri. "Control of descriptor systems." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/13531.
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Afzal, Muhammad. "Mathematical modelling of gas flow networks in pellet induratlon systems." Thesis, University of Greenwich, 1994. http://gala.gre.ac.uk/5700/.
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The objective of this research is to develop a simulation software tool, GASFLO, which should evaluate pressure, flow and temperature distributions of process gas in pellet induration system networks. Pellet induration systems are complex industrial systems composed of heterogenous components. The magnitude of gas through leaks i.e. the air entering or leaving the system from the points other than the known exits, is substantial and it adversely effects the performance of induration process. These leaks are very difficult to measure because of the hostile environment in the plant. The modelling of such industrial systems requires a notable amount of experimentation so the tool has been designed to enable the user modeller to change the component models and solution algorithms easily. The conventional methods for flow network simulation are based on process centred approach, mostly composed of homogeneous components. For ease of computation, the non-pipe elements are modelled with an approximate linear or non-linear generic equation, whose coefficients can simulate different states of the element. The resulting set of non-linear equations is linearised and solved simultaneously using some iterative method. By contrast, GASFLO is based on device centred or unit based approach, and uses a two level hierarchical solution algorithm. The pellet induration system network is first idealised into a connected graph of streams (sets of serially connected components) and nodes. At the top or coordination level the flow and pressure distributions satisfying the Kirchhoff's laws are evaluated for the connected graph. At the lower or component level the exact mathematical models of components ale computed, in order of their occurrence in respective streams, using coordination variables as parameters. The converged flows are used for the temperature computation. The solution algorithm requires partitioning of the connected graph into forest and coforest structures, for which secondary algorithms have been developed using specific heuristics relevant to the pellet induration systems. The rigorous application of software engineering techniques for the design and implementation of software, enabled the resolution of the complexity of the modelled system, embedded the characteristics of 'quality software' into the resulting code and benefits from object orientation, even though it is implemented in standard FORTRAN 77. GASFLO predicted results are in a good agreement with the measured results, it has been validated for a real life pellet induration system. It has been applied to simulate several practical scenarios, like addition of extra wind boxes to the zones and to determine how the plant production can be increased by certain ratio, such simulations were not feasible otherwise. GASFLO takes less than a minute to simulate a real-life pellet induration system on a 486 PC. The combined simulation with an other software tool, INDSYS, which evaluates the heat distribution in the solids, is also feasible.
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Zhang, Jun. "Power control in wireless ad hoc networks /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20ZHANG.
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Wredh, Simon. "Neural Network Based Model Predictive Control of Turbulent Gas-Solid Corner Flow." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420056.
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Over the past decades, attention has been brought to the importance of indoor air quality and the serious threat of bio-aerosol contamination towards human health. A novel idea to transport hazardous particles away from sensitive areas is to automatically control bio-aerosol concentrations, by utilising airflows from ventilation systems. Regarding this, computational fluid dynamics (CFD) may be employed to investigate the dynamical behaviour of airborne particles, and data-driven methods may be used to estimate and control the complex flow simulations. This thesis presents a methodology for machine-learning based control of particle concentrations in turbulent gas-solid flow. The aim is to reduce concentration levels at a 90 degree corner, through systematic manipulation of underlying two-phase flow dynamics, where an energy constrained inlet airflow rate is used as control variable. A CFD experiment of turbulent gas-solid flow in a two-dimensional corner geometry is simulated using the SST k-omega turbulence model for the gas phase, and drag force based discrete random walk for the solid phase. Validation of the two-phase methodology is performed against a backwards facing step experiment, with a 12.2% error correspondence in maximum negative particle velocity downstream the step. Based on simulation data from the CFD experiment, a linear auto-regressive with exogenous inputs (ARX) model and a non-linear ARX based neural network (NN) is used to identify the temporal relationship between inlet flow rate and corner particle concentration. The results suggest that NN is the preferred approach for output predictions of the two-phase system, with roughly four times higher simulation accuracy compared to ARX. The identified NN model is used in a model predictive control (MPC) framework with linearisation in each time step. It is found that the output concentration can be minimised together with the input energy consumption, by means of tracking specified target trajectories. Control signals from NN-MPC also show good performance in controlling the full CFD model, with improved particle removal capabilities, compared to randomly generated signals. In terms of maximal reduction of particle concentration, the NN-MPC scheme is however outperformed by a manually constructed sine signal. In conclusion, CFD based NN-MPC is a feasible methodology for efficient reduction of particle concentrations in a corner area; particularly, a novel application for removal of indoor bio-aerosols is presented. More generally, the results show that NN-MPC may be a promising approach to turbulent multi-phase flow control.
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Zaidi, Ali Abbas. "Sensing and Control of Dynamical Systems Over Networks." Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-133337.
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Rapid advances in sensing, computing, and wireless technologies have led to significant interest in the understanding and development of wireless networked control systems. Networked control systems consist of spatially distributed agents such as plants (dynamical systems), sensors, and controllers, that interact to achieve desired objectives. The sensors monitor the plants and communicate measurements to remotely situated controllers. The controllers apply actions to stabilize and control the plants. Such systems have diverse applications in security, surveillance, industrial production, health monitoring, remote surgery, environment management, space missions, and intelligent transport systems. The objective of the thesis is to understand the fundamental limits and principles involved in the design of sensing and control strategies for dynamical systems controlled over communication networks. The thesis has three parts. Part I and Part III consider the design of sensing and control strategies for mean-square stabilization of linear dynamical systems over fundamental communication channels such as point-to-point, relay, multiple-access, broadcast, and interference channels. The sensors and other nodes within the communication network are assumed to have average power transmit constraints. Moreover, the communication links between all agents (plants, sensors, controllers) are modeled as Gaussian channels. Necessary as well as sufficient conditions for mean-square stabilization over various network topologies are derived. The necessary conditions are arrived at using information theoretic arguments such as properties of mutual information, directed information, and differential entropy. The sufficient conditions are obtained using delay-free sensing and control policies. These conditions quantify the effect of communication network parameters such as transmit powers, channel noise, and channel interference on the stability of the plant(s). Different settings where linear policies are optimal, asymptotically optimal (in certain parameters of the system) and suboptimal have also been identified. Part II considers the design of real-time sensing and control strategies for minimization of a quadratic cost function of the state process of a system over Gaussian networks. Two fundamental Gaussian networks are considered: i) cascade network and ii) parallel network. For each network, non-linear sensing and control schemes are proposed and sub-optimality of linear strategies is discussed. The results reveal fundamental limits on the performance of linear systems controlled over Gaussian networks. The methods used to derive these results reveal a close interplay between information theory and control theory.<br><p>QC 20131031</p>
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Frayman, Yakov, and mikewood@deakin edu au. "Fuzzy neural networks for control of dynamic systems." Deakin University. School of Computing and Mathematics, 1999. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051017.145550.
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This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty.
Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective.
A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated.
Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior.
In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.
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Mazumdar, Sanjay Kumar. "Adaptive control of nonlinear systems using neural networks /." Title page, contents and abstract only, 1995. http://web4.library.adelaide.edu.au/theses/09PH/09phm476.pdf.
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Hofer, Daniel G. Sbarbaro. "Connectionist feedforward networks for control of nonlinear systems." Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390248.
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Tzirkel-Hancock, Eli. "Stable control of nonlinear systems using neural networks." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259554.
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Asgari, Hamid. "Modelling, Simulation and Control of Gas Turbines Using Artificial Neural Networks." Thesis, University of Canterbury. Mechanical Engineering, 2014. http://hdl.handle.net/10092/9355.
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This thesis investigates novel methodologies for modelling, simulation and control of gas turbines using ANNs. In the field of modelling and simulation, two different types of gas turbines are modelled and simulated using both Simulink and neural network based models. Simulated and operational data sets are employed to demonstrate the capability of neural networks in capturing complex nonlinear dynamics of gas turbines. For ANN-based modelling, the application of both static (MLP) and dynamic (NARX) networks are explored. Simulink and NARX models are set up to explore both steady-state and transient behaviours.
To develop an offline ANN-based system identification methodology for a low-power gas turbine, comprehensive computer program code including 18720 different ANN structures is generated and run in MATLAB to create and train different ANN models with feedforward multi-layer perceptron (MLP) structure. The results demonstrate that the ANN-based method can be applied accurately and reliably for the system identification of gas turbines.
In this study, Simulink and NARX models are created and validated using experimental data sets to explore transient behaviour of a heavy-duty industrial power plant gas turbine (IPGT). The results show that both Simulink and NARX models successfully capture dynamics of the system. However, NARX approach can model gas turbine behaviour with a higher accuracy compared to Simulink approach. Besides, a separate complex model of the start-up operation of the same IPGT is built and verified by using NARX models. The models are set up and verified on the basis of measured time-series data sets. It is observed that NARX models have the potential to simulate start-up operation and to predict dynamic behaviour of gas turbines.
In the area of control system design, a conventional proportional-integral-derivative (PID) controller and neural network based controllers consisting of ANN-based model predictive (MPC) and feedback linearization (NARMA-L2) controllers are designed and employed to control rotational speed of a gas turbine. The related parameters for all controllers are tuned and set up according to the requirements of the controllers design. It is demonstrated that neural network based controllers (in this case NARMA-L2) can perform even better than conventional controllers. The settling time, rise time and maximum overshoot for the response of NARMA-L2 is less than the corresponding factors for the conventional PID controller. It also follows the input changes more accurately than the PID.
Overall, it is concluded from this thesis that in spite of all the controversial issues regarding using artificial neural networks for industrial applications, they have a high and strong potential to be considered as a reliable alternative to the conventional modelling, simulation and control methodologies. The models developed in this thesis can be used offline for design and manufacturing purposes or online on sites for condition monitoring, fault detection and trouble shooting of gas turbines.
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Terelius, Håkan. "Optimization and Control in Dynamical Network Systems." Doctoral thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-194205.
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Dynamical network systems are complex interconnected systems useful to describe many real world problems. The advances in information technology has led the current trend towards connecting more and more systems, creating "intelligent" systems, where the intelligence originates in the scale and complexity of the network. With the growing scale of networked systems comes also higher demands on performance and continuous availability and this creates the need for optimization and control of network systems. This thesis makes four important contributions in this area. In the first contribution, we consider a collaborative road freight transportation system. An efficiency measure for the road utilization in collaborative transportation scenarios is introduced, which evaluates the performance of collaboration strategies in comparison to an optimal central planner. The efficiency measure is used to study a freight transport simulation in Germany and taxi trips using real data from New York City. This is followed by a study of the optimal idling locations for trucks, and the optimal locations for distribution centers. These locations are then exploited in a simulation of a realistic collaborative freight transport system. The second contribution studies the important problem of gathering data that are distributed among the nodes in an anonymous network, i.e., a network where the nodes are not endowed with unique identifies. Two specific tasks are considered: to estimate the size of the network, and to aggregate the distribution of local measurements generated by the nodes. We consider a framework where the nodes require anonymity and have restricted computational resources. We propose probabilistic algorithms with low resource requirements, that quickly generate arbitrarily accurate estimates. For dynamical networks, we improve the accuracy through a regularization term which captures the trade-off between the reliability of the gathered data and a-priori assumptions for the dynamics. In the third contribution, a peer-to-peer network is utilized to improve a live-streaming media application. In particular, we study how an overlay network, constructed from simple preference functions, can be used to build efficient topologies that reduce both network latency and interruptions. We present necessary and sufficient convergence conditions, as well as convergence rate estimates, and demonstrate the improvements for a real peer-to-peer video streaming application. The final contribution is a distributed optimization algorithm. We consider a distributed multi-agent optimization problem of minimizing the sum of convex objective functions. A decentralized optimization algorithm is introduced, based on dual decomposition, together with the subgradient method for finding the optimal solution. The convergence rate is analyzed for different step size rules, constant and time-varying communication delays, and noisy communication channels.<br>Dynamiska nätverkssystem är komplexa sammankopplade system med många praktiska tillämpningar. Den snabba utvecklingen inom informationsteknologin har drivit trenden att sammankoppla större och större system till nätverk av "intelligenta" system, där intelligensen kommer från komplexiteten av nätverken. Med den ökande storleken på nätverkssystemen kommer också ökade krav på dess prestanda och tillgänglighet, vilket är drivkraften bakom utvecklingen av optimering och styrning av nätverkssystem. Den här avhandlingen presenterar fyra viktiga bidrag inom detta område. Det första bidraget handlar om kooperativ lastbilstransport. Först introduceras ett mått som mäter effektiviteten i systemet jämfört med en central planerare. Detta mått används sedan för att utvärdera vinsterna med kooperativa transporter, men används också för att utvärdera taxiförarnas vägval med verkliga data från New York City. Detta följs av en studie av de optimala vänteplatserna för lastbilar och de optimal placeringarna av distributionscentraler. Dessa positioner används sedan för att förbättra transportprestandan i ett kooperativt transportsystem. I det andra bidraget studeras informationsaggregering i anonyma nätverkssystem, det vill säga nätverk där noderna saknar unika identiteter. Två specifika problem hanteras: att estimera storleken på nätverket, och att sammanställa fördelningen av lokala mätvärden i nätverket. Noderna i detta nätverk kräver anonymitet, men antas också ha strikt begränsad beräkningskapacitet. Vi presenterar stokastiska algoritmer med låga beräkningskrav, som dessutom har snabb konvergens och som kan justeras till att ge godtycklig precision. För dynamiska nätverk förbättras prestandan genom att en regulariseringsterm används för att väga observerad data mot förväntat beteende hos systemet. I tredje bidraget analyseras ett peer-to-peer nätverk för direktsänd videodistribution. Speciellt studeras konvergensen av nätverkstopologin som genereras från lokala preferensfunktioner, och hur resultaten kan används för att minska fördröjningarna och avbrotten under videouppspelning. Vi ger nödvändiga och tillräckliga villkor för konvergens, samt karakteriserar gränsvärden för hur snabbt användare kan ansluta eller lämna nätverket utan att påverka prestandan. Det sista bidraget är en distribuerad optimeringsalgoritm. Problemet består i att minimera summan av konvexa funktioner för varje nod i ett nätverk. En decentraliserad optimeringsalgoritm presenteras som baseras på det duala optimeringsproblemet tillsammans med subgradient-metoden. Konvergenshastigheten analyseras för olika val av steglängder, konstanta samt tidsberoende kommunikationsfördröjningar och brusiga kommunikationskanaler.<br><p>QC 20161020</p>
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Joe, Inwhee. "Error control for wireless ATM networks." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15643.
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Dutkiewicz, Eryk. "Connection admission control in ATM networks /." Title page, abstract and contents only, 1991. http://web4.library.adelaide.edu.au/theses/09SM/09smd976.pdf.
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Lightbody, G. "Identification and control using neural networks." Thesis, Queen's University Belfast, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359059.
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Ji, Kun. "Real-time control over networks." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5834.
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A control system in which sensors, actuators, and controllers are interconnected over a
communication network is called a networked control system (NCS). Enhanced computational
capabilities and bandwidths in the networking technology enabled researchers to develop NCSs
to implement distributed control schemes. This dissertation presents a framework for the
modeling, design, stability analysis, control, and bandwidth allocation of real-time control over
networks. This framework covers key research issues regarding control over networks and can
be the guidelines of NCS design. A single actuator ball magnetic-levitation (maglev) system is
implemented as a test bed for the real-time control over networks to illustrate and verify the
theoretical results of this dissertation. Experimentally verifying the feasibility of Internet-based
real-time control is another main objective of this dissertation.
First, this dissertation proposes a novel NCS model in which the effects of the networkinduced
time delay, data-packet loss, and out-of-order data transmission are all considered.
Second, two simple algorithms based on model-estimator and predictor- and timeout-scheme are
proposed to compensate for the network-induced time delay and packet loss simultaneously.
These algorithms are verified experimentally by the ball maglev test bed. System stability analyses of original and compensated systems are presented. Then, a novel co-design
consideration related to real-time control and network communication is also proposed. The
working range of the sampling frequency is determined by the analysis of the system stability
and network parameters such as time delay, data rate, and data-packet size. The NCS design
chart developed in this dissertation can be a useful guideline for choosing the network and
control parameters in the design of an NCS. Using a real-time operating system for real-time
control over networks is also proposed as one of the main contributions of this dissertation.
After a real-time NCS is successfully implemented, advanced control theories such as
robust control, optimal control, and adaptive control are applied and formulated to improve the
quality of control (QoC) of NCSs. Finally, an optimal dynamic bandwidth management method
is proposed to solve the optimal network scheduling and bandwidth allocation problem when
NCSs are connected to the same network and are sharing the network resource.
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Ismael, Ali. "Neural adaptive control systems /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9901244.
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Grant, Marcus Paul. "Computer control of air-gas ratio for nozzle mix systems." Thesis, Coventry University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390222.
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Hu, Wenshan. "Design of networked control systems and global Web-based control laboratory." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/design-of-networked-control-systems-and-global-webbased-control-laboratory(0c8a2649-120a-494f-a887-defcaceed570).html.
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This thesis mainly focuses on two Internet based control applications. One is a networked control system (NCS) where the control loop is closed through the network. The other is the Networked Control System Laboratory (NCSLab) which is a global web based remote experimentation platform. The contribution in the first part of the thesis relates to the networked predictive control (NPC) which was first introduced by Liu et al. (2004). In this method, the controller uses the model predictive approach to predict a future control sequence and send it to the plant in a network packet. The plant side receives this packet and then determine the appropriate control signal to apply to the actuator according to the time delay measurement. This method is innovative and works well in theory, but it has two deficiencies in practical applications. The first deficiency is that it needs synchronization between the controller and plant side to measure the individual forward and feedback channel time delays and this is very hard to achieve on the Internet. In this thesis, a round-trip NPC is proposed in which the predictive calculations and signal selections are based on the round-trip delay. The measurement of the round-tip delay is achieved using the plant side clock only, so that the need for synchronization is avoided. The second deficiency is that the mathematical model has to be accurately known. Otherwise the accuracy of the predictive calculation is affected, which may result in a degraded control performance. An event-driven NPC scheme is introduced to solve this problem. In this scheme, the selection of the appropriate control signal is not based on the time delay measurement but on the previous system output. This method can compensate for the effect of model uncertainty, which has been verified by both simulations and real-time experiments. Some experiments carried out on other NPC schemes are also reported on in this thesis. They are the NPC in state-space form and nonlinear NPC. These methods expand the use of NPC methodology. The second part of the thesis describes the design and implementation of the Networked Control System Laboratory. The NCSLab is based in the University of Glamorgan but its test rigs are diversely located in four Institutions from both the UK and China. In order to manage these test rigs from different places, a four layer structure (Central Server/Regional Server/Sub-Server/Test Rig) is adopted. The four layers are integrated into one system via the Internet. In order to deliver the remote experimentation to the users, a web-based user interface is designed. It provides great flexibility to the users such as remote monitoring, remote tuning and remote control algorithms. The implementation of the user interface (UI) heavily adopts the AJAX technology, so the remote experiments can be conducted inside the web browsers without installing special plug-ins. In order to show how the NCSLab works, two examples are given in the thesis.
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Bu, Shengrong. "Wireless ad-hoc control networks." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060316.151756/index.html.
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Wang, Bo. "Analysis and implementation of time-delay systems and networked control systems." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/analysis-and-implementation-of-timedelay-systems-and-networked-control-systems(f54e4378-db9b-443d-b505-88b4af5bb72a).html.
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Systems with delays frequently appear in engineering. The presence of delays makes system analysis and control design much more complicated. Networked control systems where the delays are often random are typical cases of such systems. For one particular category of time-delays systems, integral processes with dead time (IPDTs), the control limits that a PI controller can achieve are discussed in this thesis. These limits include the region of the control parameters to guarantee the system stability, the control parameters to achieve the given gain and/or phase margins (GPMs), the constraint on achievable gain and phase margins, the performance of set point tracking and disturbance rejection. Three types of PI controllers, namely typical PI controller, single tuning-parameter PI controller and PI controller under two-degree-of-freedom (2-DOF) structure, are studied. In control schemes of the modified Smith predictor (MSP) where the controller usually includes a distributed delay, the system implementation is not trivial because of the inherent hidden unstable poles. This thesis provides an estimation of the minimal number of implementation steps for the distributed delay in linear control laws. This is obtained by solving an inequality with respect to the number of implementation steps. A coarse estimation is given as the initial value to solve the inequality using bisection algorithms. A minimization process as well as some other techniques are also introduced to further improve the estimation. In networked control systems, the network-transmission delay and data dropout are combinedly represented by a network-induced delay. By designing a data pre processing mechanism, the network-induced delay can be assigned. Such delay as signment is applied to networked predictive control schemes, which alleviates systemstability limits on the network-induced delay. Two stability criteria are given for the closed-loop system with random network-induced delay, and a resulting implementation algorithm is also provided. The control and implementation of a magnetic levitation system over the network is studied in this thesis. Firstly, a test-rig which is suitable to implement control over a network is set up. Feedback linearization and direct local linearization methods for the nonlinear MagLev system are presented. In order to improve the control performance, a networked predictive method is employed, where the system model is identified in real-time. Local control and networked control are implemented on this test-rig, including networked predictive control. Model predictive control demonstrates a clear performance advantage over the networked control strategies which does not incorporate compensation for the network-induced delay. In order to quickly implement networked control systems (NCSs) by simulation or practical application, a MATLAB/Simulink based NCS toolbox is developed. This toolbox incorporates basic parts of a general NCS, that is, network simulation, network interface, plant interface and typical control schemes. With the NCS toolbox, users can focus on the study of new control schemes.
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Morles, Eliezer Colina. "Identification and control of dynamic systems using neural networks." Thesis, University of Sheffield, 1994. http://etheses.whiterose.ac.uk/15059/.
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The aim of this thesis is to contribute in solving problems related to the on-line identification and control of unknown dynamic systems using feedforward neural networks. In this sense, this thesis presents new on-line learning algorithms for feedforward neural networks based upon the theory of variable structure system design, along with mathematical proofs regarding the convergence of solutions given by the algorithms; the boundedness of these solutions; and robustness features of the algorithms with respect to external perturbations affecting the neural networks' signals. In the thesis, the problems of on-line identification of the forward transfer operator, and the inverse transfer operator of unknown dynamic systems are also analysed, and neural networks-based identification schemes are proposed. These identification schemes are tested by computer simulations on linear and nonlinear unknown plants using both continuous-time and discrete-time versions of the proposed learning algorithms. The thesis reports about the direct inverse dynamics control problems using neural networks, and contributes towards solving these problems by proposing a direct inverse dynamics neural network-based control scheme with on-line learning capabilities of the inverse dynamics of the plant, and the addition of a feedback path that enables the resulting control scheme to exhibit robustness characteristics with respect to external disturbances affecting the output of the system. Computer simulation results on the performance of the mentioned control scheme in controlling linear and nonlinear plants are also included. The thesis also formulates a neural network-based internal model control scheme with on-line estimation capabilities of the forward transfer operator and the inverse transfer operator of unknown dynamic systems. The performance of this internal model control scheme is tested by computer simulations using a stable open-loop unknown plant with output signal corrupted by white noise. Finally, the thesis proposes a neural network-based adaptive control scheme where identification and control are simultaneously carried out.
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Brown, Lars, and Erik Axelsson. "Use of Information-Centric Networks in Revision Control Systems." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-31063.
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NetInf and CCN are two Information-Centric Network approaches which are constructed to solve limitations of today’s Internet that was developed in the 60’s. Today’s Internet requires that datum is referred by its location. This is not something the end-user is interested in, the user is only interested in securely finding the information that searched for. These approaches decouple location from data and also aim on embedding security into the information itself and to provide caching functionality directly in networks. The main goal of this thesis work was to show the potential advantages of using the Information-Centric approaches by implementing them into the version control system Subversion and performing experimental evaluations. A Subversion adaptation has successfully been developed which supports both OpenNetInf and CCNx. With a 1Mbit/s connection to the Subversion server evaluation results show that, compared to the original implementation, checkouts can be performed 13(!) times faster using OpenNetInf and 2.3 times faster using CCNx. This work also presents motivation for future work in the area of Information-Centric Networks and has contributed with a working application which exploits advantages of these approaches.
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Clegg, Stephen. "Integrated modelling of electrical and gas transmission networks in multi-energy systems." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/integrated-modelling-of-electrical-and-gas-transmission-networks-in-multienergy-systems(e1b07a2b-8bd2-472d-8274-8d54978e9770).html.
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The scope of this thesis is an investigation into the interactions between the gas and electrical transmission networks in the context of a low-carbon energy system with explicit considerations of the role played by multi-energy vectors, including heating, in future scenarios. Many energy systems are in a state of transition due to the growing need to reduce their carbon impact while maintaining reliability and reducing costs. The generation capacity of gas-fuelled power stations, as a cleaner alternative to coal, has been continuing to grow in many power systems. Furthermore, their operational characteristics are evolving as they are increasingly used to meet demand when there is a shortfall in renewable generation and are playing a role in contributing to the reliability of the power system. Additionally, changes to the heating sector (e.g., the electrification of heat or the increased use of combined heat-and-power) and the introduction of power-to-gas (to convert excess renewable electricity into hydrogen for successive energy generation) all lead to tighter interactions between the heat, gas and power sectors which require a multi-energy framework to assess. In this work this is achieved, firstly, through the development of integrated gas and power network modelling techniques. The power system modelling incorporates a multi-temporal DC optimal power flow, while gas network models use steady-state and transient flow analysis to allow for an assessment of the pressures and flows around the network. Additionally, a novel heat model is presented for a regional assessment of the heating demands of the British energy system, so that the impacts of changes to heating technologies on the gas and electrical transmission networks can be quantified in whole-energy system case studies. Power-to-gas technologies where (excess renewable) electrical energy is converted to hydrogen and then potentially synthetic natural gas which is then injected into the gas network where it can be stored and transported have the potential to increase the integration of renewable resources and reduce the carbon impact of both electricity and heating sectors. Models are presented which assess the operation of power-to-gas and its impact on the gas and electrical transmission networks as well as the benefits to the energy system. This uses power system modelling to assess the excess renewable energy (from wind and solar resources) that can be used in the power-to-gas process, with the amounts of produced hydrogen and synthetic natural gas also being evaluated considering constraints imposed by the gas network. The resulting case studies evaluate the impact on the gas and electrical networks and the benefit to the energy system by displacing natural gas and reducing carbon emissions. To assess the extent to which gas power stations can change their output to follow changes in renewable generation output, an integrated gas and electrical network flexibility model has been developed. This uses the notion of zonal linepack flexibility and allows for limits to be applied to the ability of gas turbines to change their output over the forthcoming hours with consideration of the intermittent nature of renewable generation. The developed models and methodologies are incorporated into a number of case studies using the British gas and electrical networks and heating sector showing their applicability to assessing future energy system needs.
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McFarland, Michael Bryan. "Adaptive nonlinear control of missiles using neural networks." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13283.
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林誠 and Shing Lam. "Stability of neural network control systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31214265.
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Lam, Shing. "Stability of neural network control systems /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1859797X.
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Ramani, Vipin. "Reconfigurable control using polynomial neural networks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13297.
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Watanabe, Yukio. "Learning control of automotive active suspension systems." Thesis, Cranfield University, 1997. http://dspace.lib.cranfield.ac.uk/handle/1826/13865.
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This thesis considers the neural network learning control of a variable-geometry automotive active suspension system which combines most of the benefits of active suspension systems with low energy consumption. Firstly, neural networks are applied to the control of various simplified automotive active suspensions, in order to understand how a neural network controller can be integrated with a physical dynamic system model. In each case considered, the controlled system has a defined objective and the minimisation of a cost function. The neural network is set up in a learning structure, such that it systematically improves the system performance via repeated trials and modifications of parameters. The learning efficiency is demonstrated by the given system performance in agreement with prior results for both linear and non-linear systems. The above simulation results are generated by MATLAB and the Neural Network Toolbox. Secondly, a half-car model, having one axle and an actuator on each side, is developed via the computer language, AUTOSIM. Each actuator varies the ratio of the spring/damper unit length change to wheel displacement in order to control each wheel rate. The neural network controller is joined with the half-car model and learns to reduce the defined cost function containing a weighted sum of the squares of the body height change, body roll and actuator displacements. The performances of the neurocontrolled system are compared with those of passive and proportional-plusdifferential controlled systems under various conditions. These involve various levels of lateral force inputs and vehicle body weight changes. Finally, energy consumption of the variable-geometry system, with either the neurocontrol or proportional-plus-differential control, is analysed using an actuator model via the computer simulation package, SIMULINK. The simulation results are compared with those of other actively-controlled suspension systems taken from the literature.
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Bensing, Richard G. "An assessment of vulnerabilities for ship-based control systems." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Sep/09Sep%5FBensing.pdf.
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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2009.<br>Thesis Advisor(s): Burke, Karen ; Dinolt, George. "September 2009." Description based on title screen as viewed on November 04, 2009. Author(s) subject terms: Vulnerability Assessment, Supervisory Control and Data Acquisition, SCADA, Critical Infrastructure, Information Assurance, control system Includes bibliographical references (p. 161-170). Also available in print.
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Heffernan, Donal A. "Embedded real-time networks for control applications." Thesis, University of Salford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272619.
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Zhang, Bing. "Experiments in learning control using neural networks." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336066.
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Biss, Diane. "Gas turbine control using polynomial H#infinity# design techniques." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307025.
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Tai, Hoi-lun Allen. "Stochastic models for inventory systems and networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37681758.
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Chen, Jiancong. "Mixed-mode wireless networks : framework and power control issues /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20CHEN.
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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 101-108). Also available in electronic version. Access restricted to campus users.
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Guo, Lingzhong. "Applications of neural networks in nonlinear dynamic systems." Thesis, University of the West of England, Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275830.
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Foster, Austin Richard. "Thermal Gradient Characterization and Control in Micro-Fabricated Gas Chromatography Systems." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7481.
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In order to make gas chromatography (GC) more widely accessible, considerable effort has been made in developing miniaturized GC systems. Thermal gradient gas chromatograpy (TGGC), one of the heating methods used in GC, has recieved attention over the years due to it's ability to enhance analyte focusing. The present work seeks to develop high performance miniaturized GC systems by combining miniaturized GC technology with thermal gradient control methods, creating miniaturized thermal gradient gas chromatography (µTGGC) systems. To aid in this development a thermal control system was developed and shown to successfully control various µTGGC systems. DAQ functionality was also included which allowed for the recording of temperature and power data for use in modeling applications. Thermal models of the various µTGGC systems were developed and validated against the recorded experiemental data. Thermal models were also used to aid in decisions required for the development of new µTGGC system designs. The results from the thermal models were then used to calibrate and validate a stochastic GC transport model. This transport model was then used to evaluate the effect of thermal gradient shape on GC separation performance.
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Naraghi, F. "Communication protocols for distributed monitoring and control systems." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280539.
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Javali, Nagesh. "Topology control for wireless ad-hoc networks." Click here for download, 2008. http://proquest.umi.com/pqdweb?did=1580780361&sid=1&Fmt=2&clientId=3260&RQT=309&VName=PQD.
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Thesis (M.S.)--Villanova University, 2008.<br>"This research work is funded in part by National Science Foundation (NSF), Computing and Communication Foundation (CCF) award 0728909"--P. iii. Computer Science Dept. Includes bibliographical references.
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Feng, Ming. "Local modelling and control of nonlinear systems." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326788.
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