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Dissertations / Theses on the topic 'Networked Nonlinear Control Systems'

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Published: 4 June 2021

Last updated: 14 February 2022

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1

Costello, Zachary Kohl. "Distributed computation in networked systems." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54924.

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The objective of this thesis is to develop a theoretical understanding of computation in networked dynamical systems and demonstrate practical applications supported by the theory. We are interested in understanding how networks of locally interacting agents can be controlled to compute arbitrary functions of the initial node states. In other words, can a dynamical networked system be made to behave like a computer? In this thesis, we take steps towards answering this question with a particular model class for distributed, networked systems which can be made to compute linear transformations.

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2

Ogidan, Olugbenga Kayode. "Design of nonlinear networked control for wastewater distributed systems." Thesis, Cape Peninsula University of Technology, 2014. http://hdl.handle.net/20.500.11838/1201.

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Thesis submitted in fulfilment of the requirements for the degree Doctor of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2014<br>This thesis focuses on the design, development and real-time simulation of a robust nonlinear networked control for the dissolved oxygen concentration as part of the wastewater distributed systems. This concept differs from previous methods of wastewater control in the sense that the controller and the wastewater treatment plants are separated by a wide geographical distance and exchange data through a communication medium. The communication network introduced between the controller and the DO process creates imperfections during its operation, as time delays which are an object of investigation in the thesis. Due to the communication network imperfections, new control strategies that take cognisance of the network imperfections in the process of the controller design are needed to provide adequate robustness for the DO process control system. This thesis first investigates the effects of constant and random network induced time delays and the effects of controller parameters on the DO process behaviour with a view to using the obtained information to design an appropriate controller for the networked closed loop system. On the basis of the above information, a Smith predictor delay compensation controller is developed in the thesis to eliminate the deadtime, provide robustness and improve the performance of the DO process. Two approaches are adopted in the design of the Smith predictor compensation scheme. The first is the transfer function approach that allows a linearized model of the DO process to be described in the frequency domain. The second one is the nonlinear linearising approach in the time domain. Simulation results reveal that the developed Smith predictor controllers out-performed the nonlinear linearising controller designed for the DO process without time delays by compensating for the network imperfections and maintaining the DO concentration within a desired acceptable level. The transfer function approach of designing the Smith predictor is found to perform better under small time delays but the performance deteriorates under large time delays and disturbances. It is also found to respond faster than the nonlinear approach. The nonlinear feedback linearisig approach is slower in response time but out-performs the transfer function approach in providing robustness and performance for the DO process under large time delays and disturbances. The developed Smith predictor compensation schemes were later simulated in a real-time platform using LabVIEW. The Smith predictor controllers developed in this thesis can be applied to other process control plants apart from the wastewater plants, where distributed control is required. It can also be applied in the nuclear reactor plants where remote control is required in hazardous conditions. The developed LabVIEW real-time simulation environment would be a valuable tool for researchers and students in the field of control system engineering. Lastly, this thesis would form the basis for further research in the field of distributed wastewater control.

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3

Ouyang, Hua. "Networked predictive control systems : control scheme and robust stability." Thesis, University of South Wales, 2007. https://pure.southwales.ac.uk/en/studentthesis/networked-predictive-control-systems(9c6178d7-e6a4-420b-b35f-2d62d35ff5b0).html.

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Networked predictive control is a new research method for Networked Control Systems (NCS), which is able to handle network-induced problems such as time-delay, data dropouts, packets disorders, etc. while stabilizing the closed-loop system. This work is an extension and complement of networked predictive control methodology. There is always present model uncertainties or physical nonlinearity in the process of NCS. Therefore, it makes the study of the robust control of NCS and that of networked nonlinear control system (NNCS) considerably important. This work studied the following three problems: the robust control of networked predictive linear control systems, the control scheme for networked nonlinear control systems (NNCS) and the robust control of NNCS. The emphasis is on stability analysis and the design of robust control. This work adapted the two control schemes, namely, the time-driven and the event driven predictive controller for the implementation of NCS. It studied networked linear control systems and networked nonlinear control systems. Firstly, time-driven predictive controller is used to compensate for the networked-induced problems of a class of networked linear control systems while robustly stabilizing the closed-loop system. Secondly, event-driven predictive controller is applied to networked linear control system and NNCS and the work goes on to solve the robust control problem. The event-driven predictive controller brings great benefits to NCS implementation: it makes the synchronization of the clocks of the process and the controller unnecessary and it avoids measuring the exact values of the individual components of the network induced time-delay. This work developed the theory of stability analysis and robust synthesis of NCS and NNCS. The robust stability analysis and robust synthesis of a range of different system configurations have been thoroughly studied. A series of methods have been developed to handle the stability analysis and controller design for NCS and NNCS. The stability of the closed-loop of NCS has been studied by transforming it into that of a corresponding augmented system. It has been proved that if some equality conditions are satisfied then the closed-loop of NCS is stable for an upper-bounded random time delay and data dropouts. The equality conditions can be incorporated into a sub-optimal problem. Solving the sub-optimal problem gives the controller parameters and thus enables the synthesis of NCS. To simplify the calculation of solving the controller parameters, this thesis developed the relationship between networked nonlinear control system and a class of uncertain linear feedback control system. It proves that the controller parameters of some types of networked control system can be equivalently derived from the robust control of a class of uncertain linear feedback control system. The methods developed in this thesis for control design and robustness analysis have been validated by simulations or experiments.

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4

Varutti, Paolo [Verfasser]. "Model Predictive Control for Nonlinear Networked Control Systems : A Model-based Compensation Approach for Nondeterministic Communication Networks / Paolo Varutti." Aachen : Shaker, 2014. http://d-nb.info/1053361688/34.

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5

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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6

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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7

Cheung, Wan Sup. "Identification, stabilisation and control of nonlinear systems using neural network-based parametric nonlinear modelling." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333732.

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8

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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9

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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10

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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11

Hayakawa, Tomohisa. "Direct Adaptive Control for Nonlinear Uncertain Dynamical Systems." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5292.

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In light of the complex and highly uncertain nature of dynamical systems requiring controls, it is not surprising that reliable system models for many high performance engineering and life science applications are unavailable. In the face of such high levels of system uncertainty, robust controllers may unnecessarily sacrifice system performance whereas adaptive controllers are clearly appropriate since they can tolerate far greater system uncertainty levels to improve system performance. In this dissertation, we develop a Lyapunov-based direct adaptive and neural adaptive control framework that addresses parametric uncertainty, unstructured uncertainty, disturbance rejection, amplitude and rate saturation constraints, and digital implementation issues. Specifically, we consider the following research topics: direct adaptive control for nonlinear uncertain systems with exogenous disturbances; robust adaptive control for nonlinear uncertain systems; adaptive control for nonlinear uncertain systems with actuator amplitude and rate saturation constraints; adaptive reduced-order dynamic compensation for nonlinear uncertain systems; direct adaptive control for nonlinear matrix second-order dynamical systems with state-dependent uncertainty; adaptive control for nonnegative and compartmental dynamical systems with applications to general anesthesia; direct adaptive control of nonnegative and compartmental dynamical systems with time delay; adaptive control for nonlinear nonnegative and compartmental dynamical systems with applications to clinical pharmacology; neural network adaptive control for nonlinear nonnegative dynamical systems; passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems; neural network adaptive dynamic output feedback control for nonlinear nonnegative systems using tapped delay memory units; Lyapunov-based adaptive control framework for discrete-time nonlinear systems with exogenous disturbances; direct discrete-time adaptive control with guaranteed parameter error convergence; and hybrid adaptive control for nonlinear uncertain impulsive dynamical systems.

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12

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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13

Zuo, Wei. "Fourier neural network based tracking control for nonlinear systems /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?MECH%202008%20ZUO.

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14

Ouyang, Xiaohong. "Neural network identification and control of electrical power steering systems." Thesis, University of Wolverhampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323099.

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15

Hui, Qing. "Nonlinear dynamical systems and control for large-scale, hybrid, and network systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24635.

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Thesis (Ph.D.)--Aerospace Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Haddad, Wassim; Committee Member: Feron, Eric; Committee Member: JVR, Prasad; Committee Member: Taylor, David; Committee Member: Tsiotras, Panagiotis

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16

Delgado, Rivera Jesus Alberto. "Input/output linearization of control affine systems using neural networks." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307801.

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17

楊偉強 and Wai-keung Yeung. "Self-tuning control of nonlinear systems based on neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B42576866.

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Yeung, Wai-keung. "Self-tuning control of nonlinear systems based on neurofuzzy networks." Click to view the E-thesis via HKUTO, 2002. http://sunzi.lib.hku.hk/hkuto/record/B42576866.

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19

Nardi, Flavio. "Neural network based adaptive alogrithms for nonlinear control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/12012.

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20

Dunn, John. "An investigation into neural network assisted model predictive control for nonlinear systems." Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367442.

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21

Chan, Yat-fei. "Neurofuzzy network based adaptive nonlinear PID controllers." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43958357.

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22

Chan, Yat-fei, and 陳一飛. "Neurofuzzy network based adaptive nonlinear PID controllers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43958357.

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23

Teo, Chin Hock. "Back-propagation neural networks in adaptive control of unknown nonlinear systems." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26898.

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Approved for public release; distribution is unlimited<br>The objective of this research is to develop a Back-propagation Neural Network (BNN) to control certain classes of unknown nonlinear systems and explore the network's capabilities. The structure of the Direct Model Reference Adaptive Controller (DMRAC) for Linear Time Invariant (LTI) systems with unknown parameters is first analyzed. This structure is then extended using a BNN for adaptive control of unknown nonlinear systems. The specific structure of the BNN DMRAC is developed for control of four general classes of nonlinear systems modeled in discrete time. Experiments are conducted by placing a representative system from each class under the BNN's control. The condition under which the BNN DMRAC can successfully control these systems are investigated. The design and training of the BNN are also studied. The results of the experiments show that the BNN DMRAC works for the representative systems considered, while the conventional least-squares estimator DMRAC fails. Based on analysis and experimental findings, some genera conditions required to ensure that this technique works are postulated and discussed. General guidelines used to achieve the stability of the BNN learning process and good learning convergence are also discussed. To establish this as a general and significant control technique, further research is required to obtain analytically, the conditions for stability of the controlled system, and to develop more specific rules and guidelines in the BNN design and training.

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24

Cakarcan, Alpay. "Back-propagation neural networks in adaptive control of unknown nonlinear systems." Thesis, Monterey, California. Naval Postgraduate School, 1994. http://hdl.handle.net/10945/30830.

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The objective of this thesis research is to develop a Back-Propagation Neural Network (BNN) to control certain classes of unknown nonlinear systems and explore the network's capabilities. The structure of the Direct Model Reference Adaptive Controller (DMRAC) for Linear Time Invariant (LTI) systems with unknown parameters is first analyzed and then is extended to nonlinear systems by using BNN, Nonminimum phase systems, both linear and nonlinear, have also be considered. The analysis of the experiments shows that the BNN DMRAC gives satisfactory results for the representative nonlinear systems considered, while the conventional least-squares estimator DMRAC fails. Based on the analysis and experimental findings, some general conditions are shown to be required to ensure that this technique is satisfactory. These conditions are presented and discussed. It has been found that further research needs to be done for the nonminimum phase case in order to guarantee stability and tracking. Also, to establish this as a more general and significant control technique, further research is required to develop more specific rules and guidelines for the BNN design and training.

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25

Geng, Guang. "Modelling and control of some nonlinear processes in air-handling systems." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386699.

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26

Starsman, Raymond Scott. "A Hopfield network approach to direct adaptive control of nonlinear systems." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26576.

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27

Du, Hongliu. "Control of systems with uncertainties /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841139.

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28

Yu, Ssu-Hsin. "Model-based identification and control of nonlinear dynamic systems using neural networks." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/39609.

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29

Kasis, Andreas. "Distributed schemes for stability and optimality in power networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/270819.

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The generation, transmission and distribution of electricity underpins modern technology and constitutes a necessary element for our development and economic functionality. In the recent years, as a result of environmental concerns and technological advances, private and public investment have been steadily turning towards renewable sources of energy, resulting in a growing penetration of those in the power network. This poses additional challenges in the control of power networks, since renewable generation is in general intermittent, and a large penetration may cause frequent deviations between generation and demand, which can harm power quality and even cause blackouts. Load side participation in the power grid is considered by many a means to counterbalance intermittent generation, due to its ability to provide fast response at urgencies. Industrial loads as well as household appliances, may respond to frequency deviations by adjusting their demand in order to support the network. This is backed by the development of relevant sensing and computation technologies. The increasing numbers of local renewable sources of generation along the introduction of controllable loads dramatically increases the number of active elements in the power network, making traditionally implemented, centralised control dicult and costly. This demonstrates the need for the employment of highly distributed schemes in the control of generation and demand. Such schemes need to ensure the smooth and stable operation of the network. Furthermore, an issue of fairness among controllable loads needs to be considered, such that it is ensured that all loads share the burden to support the network evenly and with minimum disruption. We study the dynamic behaviour of power networks within the primary and secondary frequency control timeframes. Using tools from non-linear control and optimisation, we present methods to design distributed control schemes for generation and demand that guarantee stability and fairness in power allocation. Our analysis provides relaxed stability conditions in comparison with current literature and allows the inclusion of practically relevant classes of generation and demand dynamics that have not been considered within this setting, such as of higher order dynamics. Furthermore, fairness in the power allocation between loads is guaranteed by ensuring that the equilibria of the system are solutions to appropriately constructed optimisation problems. It is evident that a synchronising variable is required for optimality to be achieved and frequency is used as such in primary control schemes whereas for secondary frequency control a dierent synchronising variable is adopted. For the latter case, the requirements of the synchronising feedback scheme have been relaxed with the use of an appropriate observer, showing that stability and optimality guarantees are retained. The problem of secondary frequency regulation where ancillary services are provided from switching loads is also considered. Such loads switch on and off when some prescribed frequency threshold is reached in order to support the power network at urgencies. We show that the presence of switching loads does not compromise the stability of the power network and reduces the frequency overshoot, potentially saving the network from collapsing. Furthermore, we explain that when the on and o switching frequencies are equivalent, then arbitrarily fast switching phenomena might occur, something undesirable in practical implementations. As a solution to this problem, hysteresis schemes where the switch on and off frequencies differ are proposed and stability guarantees are provided within this setting.

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30

Hussain, Mohammed Azlan. "Inverse-model control strategies using neural networks : analysis, simulation and on-line implementation." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244464.

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31

Deng, Jiamei. "Predictive control of nonlinear systems using feedback linearisation based on dynamic neural networks." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433463.

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32

Sanner, Robert M. (Robert Michael). "Stable adaptive control and recursive identification of nonlinear systems using radial Gaussian networks." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12711.

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33

Lee, Seungjae. "Neural network based adaptive control and its applications to aerial vehicles." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/11957.

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34

Haynes, Barry P. "A neural network adaptive controller for non-linear systems." Thesis, University of Portsmouth, 1997. https://researchportal.port.ac.uk/portal/en/theses/a-neural-network-adaptive-controller-for-nonlinear-systems(19584462-246e-4de3-9e80-cda4923a38c1).html.

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35

Vance, Jonathan Blake. "Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Vance_09007dcc8043fb11.pdf.

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Thesis (Ph. D.)--University of Missouri--Rolla, 2007.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed March 26, 2008) Includes bibliographical references.

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36

Muga, Julius N'gon'ga. "Investigation of artificial neural networks for modeling, identification and control of nonlinear plant." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/1097.

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Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009<br>In real world systems such as the waste water treatment plants, the nonlinearities, uncertainty and complexity playa major role in their daily operations. Effective control of such systems variables requires robust control methods to accommodate the uncertainties and harsh environments. It has been shown that intelligent control systems have the ability to accommodate the system uncertain parameters. Techniques such as fuzzy logic, neural networks and genetic algorithms have had many successes in the field of control because they contain essential characteristics needed for the design of identifiers and controllers for complex systems where nonlinearities, complexity and uncertainties exist. Approaches based on neural networks have proven to be powerful tools for solvinq nonlinear control and optimisation problems. This is because neural networks have the ability to learn and approximate nonlinear functions arbitrarily wei!. The approximation capabilities of such networks can be used for the design of both identifiers and controllers. Basically, an artificial neural network is a computing architecture that consists of massively parallel interconnections of simple computing elements that provide insights into the kind of highly parallel computation that is carried out by biological nervous system. A large number of networks have been proposed and investigated with various topological structures. functionality and training algorithms for the purposes of identification and control of practical systems. For the purpose of this research thesis an approach for the investigation of the use of neural networks in identification, modelling and control of non-linear systems has been carried out. In particular, neural network identifiers and controllers have been designed for the control of the dissolved oxygen (DO) concentration of the activated sludge process in waste water treatment plants. These plants, being complex processes With several variables (states) and also affected by disturbances require some form of control in order to maintain the standards of effluent. DO concentration control in the aeration tank is the most widely used controlled variable. Nonlinearity is a feature that describes the dynamics of the dissolved oxygen process and therefore the DO estimation and control may not be sufficiently achieved with a conventional linear controller. Neural networks structures are proposed, trained and utilized for purposes of identification. modelling and design of NN controllers for nonlinear DO control. Algorithms and programs are developed using Matlab environment and are deployed on a hardware PLC platform. The research is limited to the feedforward multilayer perceptron and the recurrent neural networks for the identification and control. Control models considered are the direct inverse mode! control, internal mode! contra! and feedback linearizing control. Real-time implementation is limited to the lab-scale wastewater treatment plant.

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Shin, Yoonghyun. "Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7577.

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Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

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Madyastha, Venkatesh. "Adaptive Estimation for Control of Uncertain Nonlinear Systems with Applications to Target Tracking." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7567.

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Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances coupled to the process. The states from the adaptive EKF are used as inputs to the control law, which in target tracking usually takes the form of a guidance law. The applications of this approach lie in the areas of missile-target tracking, formation flight control and obstacle avoidance.

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Liut, Daniel Armando. "Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/29163.

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The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations. The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained. The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO).<br>Ph. D.

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40

Abdelrahim, Mahmoud. "Output feedback event-triggered control." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0110/document.

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La commande à transmissions événementielles est une approche dans laquelle les instants de transmission sont définis selon un critère dépendant de l'état du système et non plus d'une horloge à l'instar des implantations périodiques. Dans cette thèse, nous nous concentrons sur la synthèse de telles lois de commande par retour de sortie. Les contributions sont les suivantes : (i) nous proposons une méthode de synthèse dite par émulation pour des systèmes non linéaires; (ii) nous présentons une méthode de synthèse jointe de la loi de commande et de la condition de déclenchement pour les systèmes linéaires; (iii) nous nous intéressons au cas de systèmes non linéaires singulièrement perturbés et nous construisons le contrôleur à partir d’approximation de la dynamique lente uniquement<br>Event-triggered control is a sampling paradigm in which the sequence of transmission instants is determined based on the violation of a state-dependent criterion and not a time-driven clock. In this thesis, we deal with event-triggered output-based controllers to stabilize classes of nonlinear systems. The contributions of the presented material are threefold: (i) we stabilize a class of nonlinear systems by using an emulation-based approach; (ii) we develop a co-design procedure to simultaneously design the output feedback law and the event-triggering condition for linear systems; (iii) we propose stabilizing event-triggered controllers for nonlinear systems whose dynamics have two-time scales (in particular, we only rely on the knowledge of an approximate model of the slow dynamics)

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41

Kilic, Ergin. "Structured Neural Networks For Modeling And Identification Of Nonlinear Mechanical Systems." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614735/index.pdf.

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Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pattern (functional relationship) onto a generic neural network is proven to be a NP-complete problem. It implies that if training is conducted on a deterministic computer, the time required for training process grows exponentially with increasing size of the free parameter space (and the training data in correlation). As an alternative modeling technique for nonlinear dynamic systems<br>this thesis proposed a general methodology for structured neural network topologies and their corresponding applications are realized. The main idea behind this (rather classic) divide-and-conquer approach is to employ a priori information on the process to divide the problem into its fundamental components. Hence, a number of smaller neural networks could be designed to tackle with these elementary mapping problems. Then, all these networks are combined to yield a tailored structured neural network for the purpose of modeling the dynamic system under study accurately. Finally, implementations of the devised networks are taken into consideration and the efficiency of the proposed methodology is tested on four different types of mechanical systems.

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Tripathi, Nishith D. "Investigation of different approaches for identification and control of complex and nonlinear systems using neural networks." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06112009-063450/.

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43

Shankar, Praveen. "Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1186767939.

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Yang, Qinmin. "Advanced controller design using neural networks for nonlinear dynamic systems with application to micro/nano robotics." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Yang_09007dcc803cc150.pdf.

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Thesis (Ph. D.)--University of Missouri--Rolla, 2007.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 6, 2007) Includes bibliographical references.

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Farid, Farshad. "On-line modeling and inverse optimal control of a class of unknown nonlinear systems using dynamic neural networks /." Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1240704141&sid=4&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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46

Xiong, Hao. "Constrained expectation-maximization (EM), dynamic analysis, linear quadratic tracking, and nonlinear constrained expectation-maximation (EM) for the analysis of genetic regulatory networks and signal transduction networks." Thesis, [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2332.

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Opdenbosch, Patrick. "Auto-Calibration and Control Applied to Electro-Hydraulic Poppet Valves." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19758.

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Modern control design is sometimes accompanied by the challenge of dealing with nonlinear systems or plants. In some situations, due to the complexity of the plant and the unavailability of suitable models, the controls engineer opts for developing control schemes based on look-up tables. These tables, typically populated with the steady state inverse input-output characteristics of the plant, are used to compensate the plant via open-loop or closed-loop to solve the control problem. In an effort to present a new alternative, a general theoretical framework for online auto-calibration and control of general nonlinear systems is developed in this dissertation. This technique simultaneously learns the inverse input-state mapping (i.e. the calibration mapping) of the plant while forcing its state to follow a prescribed desired trajectory. The main requirements for the successful application of the novel control law are knowledge of the order of the plant and some generic data to initialize the inverse mapping. This last requirement can be easily fulfilled by using steady-state data or the equilibrium points of the plant. In this approach, the inverse mapping is learned from the current and past states. The learning is accomplished in a composite manner by employing input and state errors. The map is used simultaneously in the feedforward path to control the plant. The performance of the plant subject to this novel controller is validated through simulations and experimental data. The new control method is applied to a novel Electro-Hydraulic Poppet Valve (EHPV). These valves are used in a Wheatstone bridge arrangement for motion control of hydraulic actuators. This is preferred over the conventional use of spool valves due to the energy savings potential. It is shown in this dissertation that this method improves the value of using these types of valves for motion control in hydraulics. This is due to the combination of self-learning (auto-calibration) and better performance for a more efficient operation of hydraulic equipment. Additionally, it is shown that the auto-calibration of the valves can be used for health monitoring of the same, which consequently improves their reliability and expedites maintenance downtime.

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Andersson, Pär, and Farkhan Jamalzadeh. "Wireless networked control systems." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199310.

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Zhao, Yun-Bo. "Packet-Based Control for Networked Control Systems." Thesis, University of South Wales, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490204.

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Networked control systems (NCSs) are such control systems where the control loop is closed via some form of communication networks. These control systems are widely applicable in remote and distributed control applications. The inserted network however presents great challenges to conventional control theory as far as the design and analysis of NCSs are concerned. These challenges are caused primarily by the communication constraints in NCSs, e.g., network-induced delay, data packet dropout, data packet disorder, network access constraint, etc., which significantly degrade the system performance or even destabilize the system. When applying conventional control approaches to NCSs, considerable conservativeness is inevitable due to the failure to exploit network characteristics. Therefore, the co-design approach to NCSs in which control approaches and characteristics of NCSs are both fully considered, is believed to be the best way forward for the design of NCSs. In this thesis, we investigate the packet-based transmission of the network being used in NCSs, and propose a packet-based control (PB-control) approach to NCSs. In this approach, the 'packet' structure of data transmission in NCSs which is distinct from conventional control systems, is taken advantage of where, the control signals are first 'packed' and then sent as a sequence instead of one at a time as done in conventional control systems. \Vith the efficient use of the 'packet' structure, we can then actively compensate for the communication constraints in NCSs including the network-induced delay, data packet dropout and data packet disorder simultaneously. After determining the PB-control structure, we then extend its application to several categories of problems as follows. j Ã‚Â• The first application is to two types of special nonlinear systems described by a Hammerstein model and a Wiener model respectively. A 'two-step' approach is adopted in this situation to separate the nonlinear process from the whole system which then enables the PB-control approach to be implemented. Ã‚Â• It is observed that the communication constraints in NeSs are stochastic in nature, and thus a stochastic analysis of the PB-control approach is presented -----'''-'--'--~-Ã‚Â• .:.o'... '-~.::C''c:....'..:..'..;...';';;;'~~.~'----' ......;.''''- ---'- ..-..;.;.~~ / iii under the Markov jump system framework, by modeling the network-induced delay and data packet dropout as a homogeneous ergodic Markov chain. The sufficient and necessary conditions for stochastic stability and stabilization in this situation are also obtained. Ã‚Â• Continuous-time plant and continuous network-induced delay are observed to be more difficult to handle when implementing the PB-control approach. For this challenge, a discretization technique is introduced for the continuous network-induced delay and as a result, a novel model for NCSs is derived which is different to that obtained by conventional analysis from time delay system theory. A stabilized controller is also obtained in this situation by using delay-dependent analysis. Ã‚Â• The last application is to deal with the situation where a set of NeSs share the network and thus the network access constraint has to be considered. For this situation, a PB-control and scheduling co-design approach is proposed where, PB-control is still applied to each subsystem while scheduling algorithms are applied to schedule the network resources among the subsystems to guarantee the stability of the whole system. We also point out in the thesis that further research on the PB-control approach is still needed as far as nonlinear, continuous-time systems and stochastic analysis are concerned.

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Ji, Meng. "Graph-Based Control of Networked Systems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16313.

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Networked systems have attracted great interests from the control society during the last decade. Several issues rising from the recent research are addressed in this dissertation. Connectedness is one of the important conditions that enable distributed coordination in a networked system. Nonetheless, it has been assumed in most implementations, especially in continuous-time applications, until recently. A nonlinear weighting strategy is proposed in this dissertation to solve the connectedness preserving problem. Both rendezvous and formation problem are addressed in the context of homogeneous network. Controllability of heterogeneous networks is another issue which has been long omitted. This dissertation contributes a graph theoretical interpretation of controllability. Distributed sensor networks make up another important class of networked systems. A novel estimation strategy is proposed in this dissertation. The observability problem is raised in the context of our proposed distributed estimation strategy, and a graph theoretical interpretation is derived as well. The contributions of this dissertation are as follows: It solves the connectedness preserving problem for networked systems. Based on that, a formation process is proposed. For heterogeneous networks, the leader-follower structure is studied and sufficient and necessary conditions are presented for the system to be controllable. A novel estimation strategy is proposed for distributed sensor networks, which could improve the performance. The observability problem is studied for this estimation strategy and a necessary condition is obtained. This work is among the first ones that provide graph theoretical interpretations of the controllability and observability issues.

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