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The stringent regulations of emission levels from heavy duty vehicles create a demand for new methods for reducing harmful emissions from diesel engines. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using a selective catalyst as an aftertreatment system, utilising ammonia (NH3) for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear, since the result of the chemical reactions involved depends on the load operating point and the temperature. The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control applications in mind. The main focus of the thesis is on finding suitable techniques for effective NOx reduction without the need of over dosage of ammonia. By using data collected from a simulator together with real measured data, new black-box identification techniques are developed. Scaling and convergence properties of the proposed algorithms are analysed theoretically. Some of the resulting models are used for controller development using e.g. feedback linearisation techniques, followed by validation in a simulator environment. The benefits of nonlinear modelling and control of the SCR system are highlighted in a comparison with control based on linear models of the system. Further, a multiple model approach is investigated for simultaneous control of NOx and tailpipe ammonia. The results indicate an improvement in terms of ammonia slip reduction in comparison with models that do not take the ammonia slip into account. Another approach to NOx reduction is achieved by controlling the SCR temperature using techniques developed for LPV systems. The results indicate a reduction of the accumulated NOx.

This study presents the nonlinear design of a state space controller to control hydraulic actuators under displacement control, specifically for real-time pseudo-dynamic testing applications. The proposed control design process uses the nonlinear state space model of the dynamics of the system to be controlled; and utilizes state feedback linearization through a transformation of the state variables. Comparisons of numerical simulation results for linear state-space and nonlinear state-space controllers are given. Also robustness of the control design with respect to identified parameters is investigated. It is shown that a controller with improved performance can be designed using nonlinear state space control design techniques, provided that a representative model of the system is available. / Structural Engineering

The study of turbulence has been dominated historically by a bottom-up approach, with a much stronger emphasis on the physical structure of flows than on that of the dynam- ical state space. Turbulence has traditionally been described in terms of various visually recognizable physical features, such as waves and vortices. Thanks to recent theoretical as well as experimental advancements, it is now possible to take a more top-down approach to turbulence. Recent work has uncovered non-trivial equilibria as well as relative periodic orbits in several turbulent systems. Furthermore, it is now possible to verify theoretical results at a high degree of precision, thanks to an experimental technique known as Particle Image Velocimetry. These results squarely frame moderate Reynolds number Re turbulence in boundary shear flows as a tractable dynamical systems problem. In this thesis, I intend to elucidate the finer structure of the state space of moderate Re wall-bounded turbulent flows in hope of providing a more accurate and precise description of this complex phenomenon. Computation of new undiscovered equilibria, relative equilibria, and their heteroclinic connections provide a skeleton upon which a numerically accurate description of turbulence can be framed. The behavior of the equilibria under variation of Reynolds number and cell aspect ratios is also examined. It is hoped that this description of the state space will provide new avenues for research into nonlinear control systems for shear flows as well as quantitative predictions of transport properties of moderate Re fluid flows.

In this thesis a class of robust non-linear controllers for a gantry crane system are discussed. The gantry crane has three degrees of freedom, all of which are interrelated. These are the horizontal traverse of the cart, the vertical motion of the goods (i.e. rope length) and the swing angle made by the goods during the movement of the cart. The objective is to control all three degrees of freedom. This means achieving setpoint control for the cart and the rope length and cancellation of the swing oscillations. A mathematical model of the gantry crane system is developed using Lagrangian dynamics. In this thesis it is shown that a model of the gantry crane system can be represented as two sub models which are coupled by a term which includes the rope length as a parameter. The first system will consist of the cart and swing dynamics and the other system is the hoist dynamics. The mathematical model of these two systems will be derived independent of the other system. The model that is comprised of the two sub models is verified as an accurate model of a gantry crane system and it will be used to simulate the performance of the controllers using Matlab. For completeness a fully coupled mathematical model of the gantry crane system is also developed. A detailed design of a gain scheduled sliding mode controller is presented. This will guarantee the controller's robustness in the presence of uncertainties and bounded matched disturbances. This controller is developed to achieve cart setpoint and swing control while achieving rope length setpoint control. A non gain scheduled sliding mode controller is also developed to determine if the more complex gain scheduled sliding mode controller gives any significant improvement in performance. In the implementation of both sliding mode controllers, all system states must be available. In the real-time gantry crane system used in this thesis, the cart velocity and the swing angle velocity are not directly available from the system. They will be estimated using an alpha-beta state estimator. To overcome this limitation and provide a more practical solution an optimal output feedback model following controller is designed. It is demonstrated that by expressing the system and the model for which the system is to follow in a non-minimal state space representation, LQR techniques can be used to design the controller. This produces a dynamic controller that has a proper transfer function, and negates the need for the availability of all system states. This thesis presents an alternative method of solving the LQR problem by using a generic eigenvalue solution to solve the Riccati equation and thus determine the optimal feedback gains. In this thesis it is shown that by using a combination of sliding mode and H??? control techniques, a non-linear controller is achieved which is robust in the presence of a wide variety of uncertainties and disturbances. A supervisory controller is also described in this thesis. The supervisory control is made up of a feedforward and a feedback component. It is shown that the feedforward component is the crane operator's action, and the feedback component is a sliding mode controller which compensates as the system's output deviates from the desired trajectory because of the operator's inappropriate actions or external disturbances such as wind gusts and noise. All controllers are simulated using Matlab and implemented in real-time on a scale model of the gantry crane system using the program RTShell. The real-time results are compared against simulated results to determine the controller's performance in a real-time environment.

Bibliography : leaves 238-262. / xxiii, 262 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1995

Η παρούσα διπλωματική αφορά στον έλεγχο του ανεστραμμένου εκκρεμούς. Γίνεται μια σύντομη σύνοψη της σχετικής θεωρίας και ακολούθως ασχολείται αρχικά με τη σταθεροποίηση του γραμμικοποιημένου, γύρω από την ασταθή κατακόρυφη θέση ισορροπίας, συστήματος και στη συνέχεια με τη μελέτη δύο διαφορετικής λογικής ελέγχων για την πλήρη λύση του προβλήματος ελέγχου του εκκρεμούς, δηλαδή αφ’ ενός την ανύψωση και αφ’ ετέρου τη σταθεροποίηση του. Γίνεται και μια προσπάθεια εκτίμησης της περιοχής ελκτικότητας του συστήματος για κάποιους ελέγχους / In the present thesis the control of an inverted pendulum is presented. A brief summary of the relevant theory is presented. In the following the stabilization of the linearized system around the unstable upright position is developed. Furthermore, the study of two different control approaches for the complete solution of the pendulum; the swing up phase and stabilization phase. Lastly an effort for the rough estimation of the region of attraction is attempted

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.

Orientador: Prof. Dr. Juan Pablo Julca Ávila / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Mecânica, 2016.

Orientador: Prof. Dr. Marat Rafikov / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Mecânica, 2017. / O presente trabalho trata do controle de multiplos robos moveis enquanto mantem uma formação, atraves do uso do metodo de controle State-Dependent Riccati Equation. Seis robos com chassi diferencial são utilizados em um esquema onde um é considerado o lider e os outros cinco são considerados seguidores. Alterando parametros de formação, este trabalho busca obter cinco formações diferentes: Triangular, Echelon, Linha, Coluna e Quadrangular. Um painel de controle foi criado e simulações foram feitas utilizando o software LabVIEW, demonstrando o sucesso da aplicação do metodo de controle no problema de rastreamento de robos moveis em formação. Este trabalho tambem propõe estrategias anti-colisão para prevenir choques tanto com obstaculos quanto com outros robos. Um algoritmo é criado e implementado com sucesso para evitar colisões.