Exploiting high altitude wind energy using power kites is an emerging topic in the field of renewable energy. The claimed advantages of power kites over traditional wind power technologies are the lower construction costs, less land occupation and more importantly, the possibility of efficiently harvesting wind energy at high altitudes, where more dense and steady wind power exists. One of the most challenging issues to bring the power kite concept to real industrialization is the controller design. While traditional wind turbines can be inherently stabilized, the airborne nature of kites causes a strong instability of the systems. This thesis aims to develop a novel economic model predictive path-following control (EMPFC) framework to tackle the path-following control of power kites, as well as provide insightful stability analysis of the proposed control scheme. Chapter 3 is focused on the stability analysis of EMPFC. We proceed with a sampled-data EMPC scheme for set-point stabilization problems. An extended definition of dissipativity is introduced for continuous-time systems, followed by giving sufficient stability conditions. Then, the EMPFC scheme for output path-following problems is proposed. Sufficient conditions that guarantee the convergence of the system to the optimal operation on the reference path are derived. Finally, an example of a 2-DoF robot is given. The simulation results show that under the proposed EMPFC scheme, the robot can follow along the reference path in forward direction with enhanced economic performance, and finally converges to its optimal steady state. In Chapter 4, the proposed EMPFC scheme is applied to a challenging nonlinear kite model. By introducing additional degrees of freedom in the zero-error manifold (i.e., the space where the output error is zero), a relaxation of the optimal operation is achieved. The effectiveness of the proposed control scheme is shown in two aspects. For a static reference path, the generated power is increased while the kite is stabilized in the neighborhood of the reference path. For a dynamic reference path, the economic performance can be further enhanced since parameters for the reference path are treated as additional optimization variables. The proposed EMPFC achieves the integration of path optimization and path-following, resulting in a better economic performance for the closed-loop system. Simulation results are given to show the effectiveness of the proposed control scheme. Finally, Chapter 5 concludes the thesis and future research topics are discussed. / Graduate / 2020-05-14

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>

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.

With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths. / UOIT

There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast. This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6).<br /><br /> Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of <em>H₂</em> and <em>H_∞</em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H₂/H_&infin;</em> control problem is solved under the framework of LMIs. <br /><br /> To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. <br /><br /> Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The <em>l₂-l_&infin;</em> filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. <br /><br /> Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. <br /><br /> Finally, several open problems are listed as the future research directions.