An ideal drinking water distribution system (DWDS) must supply safe drinking water with free chlorine residual (FCR) in the form of HOCI and OCIֿ at a required concentration level. Meanwhile the FCR is consumed in the bulk liquid phase and at the DWDS pipes wall as the result of chemical reactions. Because of these, an optimized chlorine dosing for the DWDS using model-based predictive control (MBPC) is developed through the steps of modelling the FCR transport along the main pipes of the DWDS, designing chlorine dosing and implementing a multiple-input multiple-output system control scheme in Matlab 7.0.1 software. Discrete time-space models (DTSM) that can be used to predict free chlorine residual (FCR) concentration along the pipes of the DWDS over time is developed using explicit finite difference method (EFDM). Simulations of the DTSM using step and rectangular pulse input show that the effect of water flow rate velocity is much stronger than the effect of chlorine effective diffusivity coefficient on the FCR distribution and decay process in the DWDS main pipes. Therefore, the FCR axial diffusion in single pipes of the DWDS can be neglected. Investigating the effect of injection time, initial chlorine distribution, and overall chlorine decay rate constant involved in the process have provided a thorough understanding of chlorination and the effectiveness of all the parameters. This study proposed a model-based chlorine dosing design (MBCDD) based on a conventional-optimum design process (CODP) (Aurora, 2004), which is created for uncertain water demand based on the DTSM simulation. / In the MBCDD, the constraints must be met by designing distances between chlorine boosters and optimal value of the initial chlorine distribution in order to maintain the controlled variable (CV), i.e. FCR concentration with a certain degree of robustness to the variations of water flow rate. The MBCDD can cope with the simulated DWDS (SDWDS) with the conditions; the main pipe is 12 inch diameter size with the pipe length of 8.5 km, the first consumers taking the water from the point of 0.83 km, the assumed pipe wall chlorine decay rate constant of 0.45 m/day, and the value of chlorine overall decay rate constants follow Rosman's model (1994), by proposing a set of rules for selecting the locations for additional chlorine dosing boosters, and setting the optimal chlorine dosing concentrations for each booster in order to maintain a relatively even FCR distribution along the DWDS, which is robust against volumetric water supply velocity (VWS) variations. An example shows that by implementing this strategy, MBCDD can control the FCR along the 8.5 km main pipe of 12 inch diameter size with the VWS velocity from 0.2457 to 2.457 km/hr and with the assumed wall and bulk decay constants of 0.45 and 0.55 m/day, respectively. An adaptive chlorine dosing design (ACDD) as another CODP of chlorine dosing which has the same concept with the MBCDD without the rule of critical velocity is also proposed in this study. The ACDD objective is to obtain the optimum value of initial chlorine distribution for every single change in the VWS. Simulation of the ACDD on the SDWDS shows that the ACDD can maintain the FCR concentration within the required limit of 0.2-0.6 mg/1. / To enable water quality modelling for studying the effectiveness of chlorine dosing and injection in the form of mass flow rate of pure gaseous chlorine as manipulated variable (MV), a multiple-input multiple-output (MIMO) system is developed in Simulink for Matlab 7.0.1 software by considering the disturbances of temperature and circuiting flow. The MIMO system can be used to design booster locations and distribution along a main pipe of the DWDS, to monitor the FCR concentration at the point just before injection (mixing) and between two boosters, and to implement feedback and open-loop control. This study also proposed a decentralized model-based control (DMBC) based on the MBCDD-ACDD and centralized model predictive control (CMPC) in order to optimize MV to control the CV along the main pipe of the DWDS in the MIMO system from the FCR concentration at just after the chlorine injection (CVin) to the FCR concentration (CVo) before the next chlorine injection with the constraints of 0.2-0.6 ppm for both the CVin and CVo. A comparison of the performances of decentralized PI (DPI) control, DMBC and CMPC, shows that the performances of the DMBC and CMPC in controlling the MIMO system are almost the same, and they both are significantly better than the DPI control performance. In brief, model-based predictive control (MBPC), in this case a decentralized model-based control (DMBC) and a centralized predictive control (CMPC), enable optimization of chlorine dosing for the DWDS.

Development drilling is used in underground mining to create access tunnels. A common method involves using a drilling rig, known as a jumbo, to drill holes into the face of a tunnel. Jumbo drill rigs have two or more articulated arms with drills as end-effectors, that extend outwards from a vehicle. Once drilled, the holes are charged with explosives and fired to advance the tunnel. There is an ongoing imperative within the mining industry to reduce development times and reducing time spent drilling is seen as the best opportunity for achieving this. Notwithstanding that three-boom jumbos have been available for some years, the industry has maintained a preference for using jumbo rigs with two drilling booms. Three-boom machines have the potential to reduce drilling time by as much as one third, but they have proven difficult to operate and, in practice, this benefit has not been realized. The key difficulty lies in manoeuvering the booms within the tight confines of the tunnel and ensuring sequencing the drilling of holes so that each boom spends maximum time drilling. This thesis addresses the problem of optimally sequencing multi-boom jumbo drill rigs to minimize the overall time to drill a blast hole pattern, taking into account the various constraints on the problem including the geometric constraints restricting motion of the booms. The specific aims of the thesis are to: ² develop the algorithmic machinery needed to determine minimum- or near-minimum-time drill assignment for multi-boom jumbos which is suitable for "real-time" implementation; ² use this drill pattern assignment algorithm to quantify the benefits of optimal drill pattern assignment with three-boom jumbos; and ² investigate the management of unplanned events, such as boom breakdowns, and assess the potential of the algorithm to assist a human operator with the forward planning of drill-hole selection. Jumbo drill task assignment is a combinatorial optimization problem. A methodology based around receding horizon mixed integer programming is developed to solve the problem. At any time the set of drill-holes available to a boom is restricted by the location of the other booms as well as the tunnel perimeter. Importantly these constraints change as the problem evolves. The methodology builds these constraints into problem through use of a feasibility tensor that encodes the moves available to each boom given configurations of other booms. The feasibility tensor is constructed off-line using a rapidly exploring random tree algorithm. Simulations conducted using the sequencing algorithm predict, for a standard drill-hole pattern, a 10 - 22% reduction in drilling time with the three-boom rig relative to two-boom machines. The algorithms developed in this thesis have two intended applications. The first is for automated jumbo drill rigs where the capability to plan drilling sequences algorithmically is a prerequisite. Automated drill rigs are still some years from being a reality. The second, and more immediate application is in providing decision support for drill rig operators. It is envisaged that the algorithms described here might form the basis of a operator assist that provides guidance on which holes to drill next with each boom, adapting this plan as circumstances change.

La plupart des applications des avions drones sont liées à l'observation d'événements au sol. En particulier, les suivi de cibles terrestres mobiles, qu'elles soient statiques, lentes ou rapides, est une tâche essentielle pour un drone. L'objectif global de la thèse est de proposer des méthodes qui permettent à un drone de suivre une cible terrestre, dans les conditions suivantes: - Le drone est de type voilure fixe équipé d'une caméra monoculaire. - Présence d'obstacles qui occultent la visibilité de zones au sol. - Existence de zones d'exclusion aérienne qui limitent le mouvement aérien. - Restrictions sur le champ de vue du capteur qui assure le suivi (caméra) - Différents comportements de la cible : elle peut évoluer librement ou sous contraintes dynamiques (cas d'une voiture par exemple), et peut être neutre ou évasive~: dans ce dernier cas, elle peut exploiter la présence d'obstacles pour éviter d'être perçue par le drone. Trois approches pour aborder ce problème sont proposées dans la thèse : - Une méthode basée aux lois de contrôle et de la navigation, - Une méthode basée sur la prédiction des déplacements de la cible, - Et une approche basée sur la théorie des jeux. Des résultats obtenus par des simulations réalistes et avec un drone sont présentés, pour évaluer et comparer les avantages et inconvénients de chacune des approches. Des extensions au cas "multi-drones" sont aussi proposées.

<p>This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.</p>

<p>The number of MPC installations in industry is growing as a reaction to demands of increased efficiency. An MPC controller uses an internal plant model to run real-time predictive optimization of future inputs. If a discrepancy between the internal plant model and the plant exists, control performance will be affected. As time from commissioning increases the model accuracy tends to deteriorate. This is natural as the plant changes over time. It is important to detect these changes and re-identify the plant model to maintain control performance over time. A method for identifying Model Plant Mismatch for MPC applications is developed. Focus has been on developing a method that is simple to implement but still robust. The method is able to run in parallel with the process in real time. The efficiency of the method is demonstrated via representative simulation examples.An extension to detection of nonlinear mismatch is also considered, which is important since linear plant models often are used within a small operating range. Since most processes are nonlinear this discrepancy is inevitable and should be detected.</p> / <p>Ökade krav på effektivitet gör att industrin söker efter mer avancerad processtyrning. MPC har växt fram som en kandidat. En MPC regulator änvänder en modell av systemet för att samtidigt som systemet körs utföra en optimering av framtida styrsignaler. Om modellen innehåller felaktigheter kan reglerprestandan påverkas. En modell försämras normalt då tiden från idrifttagning växer eftersom systemet förändras med tiden. Det är av största vikt att upptäcka dessa förändringar och sedan uppdatera modellen för att reglerprestandan inte ska påverkas. Avsikten är att utveckla en metod för att upptäcka modellfel med fokus på att den ska vara enkel att implementera. Det ska även vara möjligt att använda metoden parallellt med en process. För att utvärdera metoden så körs den på ett antal representativa simuleringsexempel. Det har även varit en avsikt att utveckla en metod för detektion av ickelinjära modellfel. Motivet till det är att linjära modeller används för att beskriva ickelinjära processer och då är modellfel naturliga.</p>

<p>The use of positioning systems in a vehicle is a research intensive field. In the first part of this thesis an increase in new applications is disclosed through a mapping of patent documents on how positioning systems can support adaptive cruise control, gear changing systems and engine control. Many ideas are presented and explained and the ideas are valued. Furthermore, a new method for selective catalytic reduction (SCR) control using a positioning system is introduced. It is concluded that look-ahead control, where the vehicle position in relation to the upcoming road section is utilized could give better fuel efficiency, lower emissions and less brake, transmission and engine wear.</p><p>In the second part of this thesis a real time test platform for predictive speed control algorithms has been developed and tested in a real truck. Previously such algorithms could</p><p>only be simulated. In this thesis an algorithm which utilizes model predictive control (MPC) and dynamic programming (DP) been implemented and evaluated. An initial comparative fuel test shows a reduction in fuel consumption when the MPC algorithm is used.</p>

<p>The contributions of this thesis are in the area of control of systems with nonlinear dynamics. The thesis is divided into three parts. The two first parts are similar in the sense that they both consider output feedback of rather general classes of nonlinear systems, and both approaches are based on mathematical programming (although in quite different ways). The third part contains a state feedback approach for a specific system class, and is more application oriented.</p><p>The first part treats control of systems described by nonlinear difference equations, possibly with uncertain terms. The system dynamics are represented by piecewise affine difference inclusions, and for this system class, piecewise affine controller structures are suggested. Controller synthesis inequalities for such controller structures are given in the form of Bilinear Matrix Inequalities (BMIs). A solver for the BMIs is developed. The main contribution is to the output feedback case, where an observer-based controller structure is proposed. The theory is exemplified through two examples.</p><p>In the second part the output feedback problem is examined in the setting of Nonlinear Model Predictive Control (NMPC). The state space formulation of NMPC is inherently a state feedback approach, since the state is needed as initial condition for the prediction in the controller. Consequently, for output feedback it is natural to use observers to obtain estimates of the state. A high gain observer is applied for this purpose. It is shown that for several existing NMPC schemes, the state feedback stability properties ``semiglobally'' hold in the output feedback case. The theory is illuminated with a simple example.</p><p>Finally, a state feedback controller for a class of positive systems is proposed. Convergence of the state to a certain subset of the first orthant, corresponding to a constant ``total mass'' (interpreting states as masses) is obtained. Conditions are given under which convergence to this set implies asymptotic stability of an equilibrium. Simple examples illustrate some properties of the controller. Furthermore, the control strategy is applied to the stabilization of a gas-lifted oil well, and simulations on a rigorous multi-phase dynamic simulator of such a well demonstrate the controller performance.</p>

Thesis (M.Eng.)--Memorial University of Newfoundland, 2005. / Bibliography: leaves 107-116.

The contributions of this thesis are in the area of control of systems with nonlinear dynamics. The thesis is divided into three parts. The two first parts are similar in the sense that they both consider output feedback of rather general classes of nonlinear systems, and both approaches are based on mathematical programming (although in quite different ways). The third part contains a state feedback approach for a specific system class, and is more application oriented. The first part treats control of systems described by nonlinear difference equations, possibly with uncertain terms. The system dynamics are represented by piecewise affine difference inclusions, and for this system class, piecewise affine controller structures are suggested. Controller synthesis inequalities for such controller structures are given in the form of Bilinear Matrix Inequalities (BMIs). A solver for the BMIs is developed. The main contribution is to the output feedback case, where an observer-based controller structure is proposed. The theory is exemplified through two examples. In the second part the output feedback problem is examined in the setting of Nonlinear Model Predictive Control (NMPC). The state space formulation of NMPC is inherently a state feedback approach, since the state is needed as initial condition for the prediction in the controller. Consequently, for output feedback it is natural to use observers to obtain estimates of the state. A high gain observer is applied for this purpose. It is shown that for several existing NMPC schemes, the state feedback stability properties ``semiglobally'' hold in the output feedback case. The theory is illuminated with a simple example. Finally, a state feedback controller for a class of positive systems is proposed. Convergence of the state to a certain subset of the first orthant, corresponding to a constant ``total mass'' (interpreting states as masses) is obtained. Conditions are given under which convergence to this set implies asymptotic stability of an equilibrium. Simple examples illustrate some properties of the controller. Furthermore, the control strategy is applied to the stabilization of a gas-lifted oil well, and simulations on a rigorous multi-phase dynamic simulator of such a well demonstrate the controller performance.

Starting a few decades ago, the unmanned wheeled vehicle research has drawn lately more attention, especially for off-road environment. As the demand to use electric vehicles increased, the need to conceptualize the use of electrically driven vehicles in autonomous operations became a target. That is because in addition to the fact that they are more environmentally friendly, they are also easier to control. This also gives another reason to enhance further the energy economy of those unmanned electric vehicles. Off-road vehicles research was always challenging, but in the present work the nature of the off-road land is utilized to benefit from in order to enhance the energy consumption of those vehicles. An algorithm for energy consumption optimization for electrically driven unmanned wheeled vehicles is presented. The algorithm idea is based on the fact that in off-road conditions, when the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill could be utilized to reduce the energy consumption for moving uphill if the dimensions of the ditch/hole were known a distance ahead. Two manipulated variables are evaluated: the wheels DC motors supply voltage and the DC armature current. The developed algorithm is analysed and compared to the PID speed iii controller and to the open-loop control of DC motors. The developed predictive controller achieved encouraging results compared to the PID speed control and also compared to the open-loop control. Also, the use of the DC armature current as a manipulated variable showed more noticeable improvement over using the DC input voltage. Experimental work was carried out to validate the predictive control algorithm. A mobile robot with two DC motor driven wheels was deployed to overcome a ditch-like hindrance. The experimental results verified the simulation results. A parametric study for the predictive control is conducted. The effect of changing the downhill angle and the uphill angle as well as the size of the prediction horizon on the consumed electric energy by the DC motors is addressed. The simulation results showed that, when using the proposed approach, the larger the prediction horizon, the lower the energy consumption is.