<p> This thesis studies critical zones of automated vehicles. The goal is for the automated vehicle to complete a car-following or lane change maneuver without collision. For instance, the automated vehicle should be able to indicate its interest in changing lanes and plan how the maneuver will occur by using model predictive control theory, in addition to the autonomous vehicle toolbox in Matlab. A test bench (that includes a scenario creator, motion logic and planner, sensors, and radars) is created and used to calculate the parameters of a critical zone. After a trajectory has been planned, the automated vehicle then attempts the car following or lane change while constantly ensuring its safety to continue on this path. If at any point, the lead vehicle brakes or a trailing vehicle accelerates, the automated vehicle makes the decision to either brake, accelerate, or abandon the lane change. </p>

This paper presents the mathematical modeling and the design of an optimal pressure tracking controller for an often used setup in pneumatic applications. Two pneumatic chambers are connected with a pneumatic tube. The pressure in the second chamber is to be controlled using two switching valves connected to the first chamber and based on the pressure measurement in the first chamber. The optimal control problem is formulated and solved using the MPC framework. The designed controller shows good tracking quality, while fulfilling hard constraints, like maintaining the pressure below a given upper bound.

This thesis discuss the possibility to position control a rotor levitated with active magnetic bearings. The controller type considered is model predictive control which is an online strategy that solves an optimization problem in every sample, making the model predictive controller computation-intense. Since the sampling time must be short to capture the dynamics of the rotor, very little time is left for the controller to perform the optimization. Different quadratic programming strategies are investigated to see if the problem can be solved in realtime. Additionally, the impact of the choices of prediction horizon, control horizon and terminal cost is discussed. Simulations showing the characteristics of these choises are made and the result is shown. / Det här examensarbetet diskuterar möjligheten att positionsreglera en rotor som leviteras på aktiva magnetlager. Reglerstrategin som används är modellbaserad prediktionsreglering vilket är en online-metod där ett optimeringsproblem löses i varje sampel. Detta gör att regulatorn blir mycket beräkningskrävande. Samplingstiden för systemet är mycket kort för att fånga dynamiken hos rotorn. Det betyder att regulatorn inte ges mycket tid att lösa optimeringsproblemet. Olika metoder för att lösa QP-problem betraktas för att se om det är möjligt att köra regulatorn i realtid. Dessutom diskuteras hur valet av prediktionshorisont, reglerhorisont och straff på sluttillståndet påverkar regleringen. Simuleringar som visar karakteristiken av dessa val har utförts.

<div>Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under different circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap,lateral error, and steering angle simulation results between the models. Additionally,this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.</div>

With the ongoing trend in moving the upper levels of the automation hierarchy to the cloud, there has been investigation into supplying industrial automation as a cloud based service. There are many practical considerations which pose limitations on the feasibility of the idea. This research investigates some of the requirements which would be needed to implement a platform which would facilitate competition between different controllers which would compete to control a process in real-time. This work considers only the issues relating to implementation of the philosophy from a control theoretic perspective, issues relating to hardware/communications infrastructure and cyber security are beyond the scope of this work. A platform is formulated and all the relevant control requirements of the system are discussed. It is found that in order for such a platform to determine the behaviour of a controller, it would need to simulate the controller on a model of the process over an extended period of time. This would require a measure of the disturbance to be available, or at least an estimate thereof. This therefore increases the complexity of the platform. The practicality of implementing such a platform is discussed in terms of system identification and model/controller maintenance. A model of the surge tank from SibanyeStillwater’s Platinum bulk tailings treatment (BTT) plant, the aim of which is to keep the density of the tank outflow constant while maintaining a steady tank level, was derived, linearised and an input-output controllability analysis performed on the model. Six controllers were developed for the process, including four conventional feedback controllers (decentralised PI, inverse, modified inverse and H¥) and two Model Predictive Controllers (MPC) (one linear and another nonlinear). It was shown that both the inverse based and H¥ controllers fail to control the tank level to set-point in the event of an unmeasured disturbance. The competing concept was successfully illustrated on this process with the linear MPC controller being the most often selected controller, and the overall performance of the plant substantially improved by having access to more advanced control techniques, which is facilitated by the proposed platform. A first appendix presents an investigation into a previously proposed switching philosophy [15] in terms of its ability to determine the best controller, as well as the stability of the switching scheme. It is found that this philosophy cannot provide an accurate measure of controller performance owing to the use of one step ahead predictions to analyse controller behaviour. Owing to this, the philosophy can select an unstable controller when there is a stable, well tuned controller competing to control the process. A second appendix shows that there are cases where overall system performance can be improved through the use of the proposed platform. In the presence of constraints on the rate of change of the inputs, a more aggressive controller is shown to be selected so long as the disturbance or reference changes do not cause the controller to violate these input constraints. This means that switching back to a less aggressive controller is necessary in the event that the controller attempts to violate these constraints. This is demonstrated on a simple first order plant as well as the surge tank process. Overall it is concluded that, while there are practical issues surrounding plant and system identification and model/controller maintenance, it would be possible to implement such a platform which would allow a given plant access to advanced process control solutions without the need for procuring the services of a large vendor. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Recent decades have witnessed the phenomenal success of model predictive control (MPC) in a wide spectrum of domains, such as process industries, intelligent transportation, automotive applications, power systems, cyber security, and robotics. For constrained dynamic systems subject to uncertainties, robust MPC is attractive due to its capability of effectively dealing with various types of uncertainties while ensuring optimal performance concerning prescribed performance indices. But most robust MPC schemes require prior knowledge on the uncertainty, which may not be satisfied in practical applications. Therefore, it is desired to design robust MPC algorithms that proactively update the uncertainty description based on the history of inputs and measurements, motivating the development of adaptive MPC. This dissertation investigates four problems in robust and adaptive MPC from theoretical and application points of view. New algorithms are developed to address these issues efficiently with theoretical guarantees of closed-loop performance. Chapter 1 provides an overview of robust MPC, adaptive MPC, and self-triggered MPC, where the recent advances in these fields are reviewed. Chapter 2 presents notations and preliminary results that are used in this dissertation. Chapter 3 investigates adaptive MPC for a class of constrained linear systems with unknown model parameters. Based on the recursive least-squares (RLS) technique, we design an online set-membership system identification scheme to estimate unknown parameters. Then a novel integration of the proposed estimator and homothetic tube MPC is developed to improve closed-loop performance and reduce conservatism. In Chapter 4, a self-triggered adaptive MPC method is proposed for constrained discrete-time nonlinear systems subject to parametric uncertainties and additive disturbances. Based on the zonotope-based reachable set computation, a set-membership parameter estimator is developed to refine a set-valued description of the time-varying parametric uncertainty under the self-triggered scheduling. We leverage this estimation scheme to design a novel self-triggered adaptive MPC approach for uncertain nonlinear systems. The resultant adaptive MPC method can reduce the average sampling frequency further while preserving comparable closed-loop performance compared with the periodic adaptive MPC method. Chapter 5 proposes a robust nonlinear MPC scheme for the visual servoing of quadrotors subject to external disturbances. By using the virtual camera approach, an image-based visual servoing (IBVS) system model is established with decoupled image kinematics and quadrotor dynamics. A robust MPC scheme is developed to maintain the visual target stay within the field of view of the camera, where the tightened state constraints are constructed based on the Lipschitz condition to tackle external disturbances. In Chapter 6, an adaptive MPC scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. We develop an RLS-based set-membership based parameter to improve the prediction accuracy. In the proposed adaptive MPC scheme, a robustness constraint is designed to handle parametric and additive uncertainties. The proposed constraint has the offline computed shape and online updated shrinkage rate, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Chapter 7 shows some conclusion remarks and future research directions. / Graduate

Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage. / Master of Science / In today’s world, most complicated systems such as automobiles employ a decentralized modular architecture with several nodes communicating with each other over a shared medium. The Controller Area Network (CAN) is the most widely accepted standard as far as automobiles are concerned. The performance of such systems gets degraded due to the variable delays (jitters) incurred by messages on the CAN. These delays can be caused by messages of higher importance delaying bus access to the messages of lower importance, or due to other network related issues. This work presents a novel approach that predicts the message delays in real-time based on the traffic information on CAN. This approach leverages the proposed method to improve the control quality by compensating for the message delay using an advanced controller algorithm called Model Predictive Control (MPC). By simulating an automotive Cruise Control system and a DC motor plant in a CAN environment, this work goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed approach on a low end microcontroller (8bit, 16MHz ATmega328P) and measures the time taken for predicting the delay for each message (execution overhead). The obtained results clearly indicate that the method is computationally feasible for use in a real-time scenario.

L'évolution de l'électronique de puissance depuis ces dernières années est le résultat des enjeux énergétiques actuels qui exigent, entre autres, des architectures de conversion d'énergie capables de traiter des puissances de plus en plus importantes. Parmi les éléments les plus caractéristiques de cette évolution, l'avancement technologique des composants semi-conducteurs (nouveaux composants SiC ou GaN) ainsi que la conception de nouvelles architectures de convertisseurs statiques jouent un rôle important. Parmi ces architectures, différentes associations basées sur la connexion en série et en parallèle de cellules de commutation classiques ont été proposées. Ces associations permettent d'augmenter la puissance traitée par les convertisseurs sans accroitre les contraintes au niveau des interrupteurs. Elles permettent également l'obtention de signaux de sortie d'une meilleure qualité avec des fréquences apparentes de découpage plus importantes. Ces architectures utilisent des éléments de stockage d'énergie qui diminuent les contraintes au niveau des interrupteurs mais qui exigent, en revanche, une régulation précise des grandeurs de tension ou de courant propres à ces éléments. Pour l'association en série, les tensions des condensateurs doivent rester autour d'une fraction de la tension du bus d'entrée. Pour l'association en parallèle, le courant de sortie doit être réparti équitablement entre les différents bras afin d'éviter les phénomènes non linéaires propres aux éléments magnétiques utilisés dans les inductances (séparées ou magnétiquement couplées). Dans la première partie de cette thèse, nous présentons les généralités de l'association en série et parallèle des cellules de commutation. La modélisation des éléments magnétiques utilisés pour la mise en parallèle est également détaillée dans le but d'identifier de possibles sources de déséquilibre sur la répartition du courant de sortie. Une modélisation matricielle est appliquée pour simplifier la relation entre les variables propres à chaque association et les ordres de commande de toutes les cellules. Cette modélisation matricielle sera la base des stratégies de commande que nous avons développées dans la suite de nos travaux. Dans la deuxième partie de cette thèse, nous présentons les différentes stratégies de commande pouvant être appliquées sur ces convertisseurs. Les premières stratégies sont basées sur une approche classique utilisant un modulateur, un générateur d'ordres de commande et des régulateurs de type linéaire pour la régulation des variables internes et externes de chaque association. En termes de modulateurs, nous présentons principalement un modulateur de type PS (Phase Shifted), tandis que quelques applications et résultats sont présentés pour un modulateur de type PD (Phase Disposition). D'autres stratégies basées sur la commande prédictive sont également présentées. La première est la stratégie MPC qui utilise une fonction de coût pour choisir l'état optimal du convertisseur pour chaque période d'échantillonnage. Cette stratégie a été introduite récemment dans le domaine des convertisseurs statiques et présente des avantages liées à la facilité de sa mise en place ainsi qu'aux réponses du système lors des régimes transitoires. La deuxième stratégie, basée sur la commande prédictive, utilise des instants de commutation variables, une fonction de coût simplifiée et une machine d'état. Cette dernière permet de gérer les ordres de commande de toutes les cellules de commutation en fonction des variables à réguler. En plus des avantages liés à la stratégie MPC, sa mise en place est bien plus simple car elle fonctionne à une fréquence de découpage fixe et s'adapte facilement à différents points de fonctionnement. Dans la dernière partie de cette thèse, nous présentons l'implantation expérimentale de ces stratégies afin de valider leur performance sur les convertisseurs multicellulaires. / In the last years, the development in the power electronics field is the result of the current energy challenges. These challenges require power converters able to work with increasingly important powers. Among the most characteristic elements of this development, we can find the technological advancements of the semiconductor devices (based principally in SiC and GaN) and the conception of new power converters topologies. These new power converter topologies are principally based on the serial and parallel association of classical commutation cells. With these associations, the energy treated by the converter can be increased using the current semiconductor technology. The quality of the output signals can also be improved with higher apparent switching frequencies. These associations use elements for storing energy, such as inductors or capacitors. They equally allow the reduction of the constraints on the switches given the higher voltages and currents. However, the use of these elements requires a good control of the capacitors' voltage for the serial connection and a good distribution of the output current among the different phases for the parallel connection. In the parallel connection, when we use Inter Cells Transformers (ICT) instead of classical inductors, all the phase currents reduce their ripples while their frequency is reduced. Nevertheless, some differences between all the phases' currents can entail non-linear phenomena, producing perturbations and instabilities in the system. In the serial connection, the capacitor voltages must stay around a fraction of the input voltage in order to get an optimal multilevel output voltage. In the first part of this thesis, we present the generalities of the serial and parallel association of classical commutation cells. Different models of magnetic elements are used for getting a better representation of an ICT; these models are used for finding possible sources of currents imbalances. A matrix model is used to simplify the relationship between the control variables with the control of each commutation cell. In the second part of this thesis, some control strategies that can be applied with these converters are presented. The first strategy is based on a conventional approach that uses a modulator, a state machine for generating the commands of each cell and linear regulators for controlling the internal and external variables (output voltage and currents, capacitors in the serial association and the distribution of the current for the parallel connection). In terms of modulators, we present primarily a PS (Phase Shifted) modulator while some applications and results are presented for a PD (Phase Disposition) modulator. Other strategies based on predictive control are also presented. The first of these strategies is the classical MPC (model predictive control) strategy that uses a cost function to select the optimal state of the converter for each sampling period. This strategy has recently been introduced in the field of static converters and it has several advantages related to the facility of its implementation and the optimal transient responses. The second strategy uses variable switching instants, a simplified cost function and a state machine. The state machine is used to manage the capacitors' voltages and the differential currents (differences between the phase currents) while the cost function is used for controlling the output voltage and current. This strategy is simpler to be implemented, presents fast transient responses and works with a fixed switching frequency in different operating points. In the last part of this thesis, we present the experimental implementation of these strategies in order to validate their performance in the power converters based in the serial and parallel association of classical commutation cells.

The thesis focuses on the design of a control strategy for safety-critical remote teleoperation. The main goal is to make the controlled system track the desired velocity specified by a human operator while avoiding obstacles despite communication delays. Different methods adopting Control Barrier Functions (CBFs) and Model Predictive Control (MPC) have been explored and tested. In this combination, CBFs are used to define the safety constraints the system has to respect to avoid obstacles, while MPC provides the framework for filtering the desired input by solving an optimization problem. The resulting input is sent to the remote system, where appropriate low-level velocity controllers translate it into system-specific commands. The main novelty of the thesis is a method to make the CBFs robust against the uncertainties affecting the system’s state due to network delays. Other techniques are investigated to improve the quality of the system information starting from the delayed one and to formulate the optimization problem without knowing the specific dynamic of the controlled system. The results show how the proposed method successfully solves the safetycritical teleoperation problem, making the controlled systems avoid obstacles with different types of network delay. The controller has also been tested in simulation and on a real manipulator, demonstrating its general applicability when reliable low-level velocity controllers are available. / Avhandlingen fokuserar på utformningen av en kontrollstrategi för säkerhetskritisk fjärrstyrd teleoperation. Huvudmålet är att få det kontrollerade systemet att följa den önskade hastigheten som specificeras av en mänsklig operatör samtidigt som hinder undviks trots kommunikationsfördröjningar. Olika metoder som använder Control Barrier Functions (CBFs) och Model Predictive Control har undersökts och testats. I denna kombination används CBFs för att definiera de säkerhetsbegränsningar som systemet måste respektera för att undvika hinder, medan MPC utgör ramverket för filtrering av den önskade indata genom att lösa ett optimeringsproblem. Den resulterande indata skickas till fjärrsystemet, där lämpliga hastighetsregulatorer på låg nivå översätter den till systemspecifika kommandon. Den viktigaste nyheten i avhandlingen är en metod för att göra CBFs robust mot de osäkerheter som påverkar systemets tillstånd på grund av nätverksfördröjningar. Andra tekniker undersöks för att förbättra kvaliteten på systeminformationen med utgångspunkt från den fördröjda informationen och för att formulera optimeringsproblemet utan att känna till det kontrollerade systemets specifika dynamik. Resultaten visar hur den föreslagna metoden framgångsrikt löser det säkerhetskritiska teleoperationsproblemet, vilket gör att de kontrollerade systemen undviker hinder med olika typer av nätverksfördröjningar. Styrningen har också testats i simulering och på en verklig manipulator, vilket visar dess allmänna tillämpbarhet när tillförlitliga lågnivåhastighetsregulatorer finns tillgängliga.

This paper presents the mathematical modeling and the design of an optimal pressure tracking controller for an often used setup in pneumatic applications. Two pneumatic chambers are connected with a pneumatic tube. The pressure in the second chamber is to be controlled using two switching valves connected to the first chamber and based on the pressure measurement in the first chamber. The optimal control problem is formulated and solved using the MPC framework. The designed controller shows good tracking quality, while fulfilling hard constraints, like maintaining the pressure below a given upper bound.