Unmanned aerial systems have been widely used for variety of civilian applications over the past few years. Some of these applications require accurate guidance and control. Consequently, Unmanned Aerial Vehicle (UAV) guidance and control attracted many researchers in both control theory and aerospace engineering. Flying wings, as a particular type of UAV, are considered to have one of the most efficient aerodynamic structures. It is however difficult to design robust controller for such systems. This is due to the fact that flying wings are highly sensitive to control inputs. The focus of this thesis is on modeling and control design for a UAV system. The platform understudy is a flying wing developed by SmartPlanes Co. located in Skellefteå, Sweden. This UAV is particularly used for topological mapping and aerial photography. The novel approach suggested in this thesis is to use two controllers in sequence. More precisely, Linear Quadratic Regulator (LQR) is suggested to provide robust stability, and Proportional, Integral, Derivative (PID) controller is suggested to provide reference signal regulation. The idea behind this approach is that with LQR in the loop, the system becomes more stable and less sensitive to control signals. Thus, PID controller has an easier task to do, and is only used to provide the required transient response. The closed-loop system containing the developed controller and a UAV non-linear dynamic model was simulated in Simulink. Simulated controller was then tested for stability and robustness with respect to some parametric uncertainty. Obtained results revealed that the LQR successfully managed to provide robust stability, and PID provided reference signal regulation.

For more than 100 years, the Navier-Stokes equations and various linearizations have been used as a model to study fluid dynamics. Recently, attention has been directed toward studying the nonnormality of linearized problems and developing convergent numerical schemes for simulation of these sytems. Numerical schemes for optimal control problems often require additional properties that may not be necessary for simulation; these properties can be critical when studying nonnormal problems. This research is concerned with approximating infinite dimensional optimal control problems with nonnormal system operators. We examine three different finite element methods for a specific convection-diffusion equation and prove convergence of the infinitesimal generators. Additionally, for two of these schemes, we prove convergence of the associated feedback gains. We apply these three schemes to control problems and compare the performance of all three methods. / Ph. D.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / For any automated surveillance operation to be successful, it is critical to have sensing resources strategically positioned to observe, interpret, react and maybe even predict events.In many practical scenarios, it is also expected that different zones within a surveillance area may have different probability of event detection (or false alarm) requirements. The operational objective in such surveillance systems is to optimize resources (number of sensors and the associated cost) and their deployment while guaranteeing a certain assured level of detection/false alarm performance. In this dissertation, we study two major challenges related to sensor deployment in distributed sensor networks (DSNs) for detection applications. The first problem we study is the sensor deployment problem in which we ask the following question: Given a finite number of sensors (with a known sensing profile), how can we deploy these sensors such that we best meet the detection and false alarm requirements in a DSN employing a specific information fusion rule? Even though sensor deployment has garnered significant interest in the past, a unified, analytical framework to model and study sensor deployment is lacking. Additionally, the algorithms proposed in literature are typically heuristic in nature and are limited to (1) simplistic DSN fusion architectures, and (2) DSNs with uniform detection/false alarm requirements. In this dissertation, we propose a novel treatment of the sensor deployment problem using concepts from optimal control theory. Specifically, the deployment problem is formulated as a linear quadratic regulator (LQR) problem which provides a rigorous and analytical framework to study the deployment problem. We develop new sensor deployment algorithms that are applicable to a wide range of DSN architectures employing different fusion rules such as (1) logical OR fusion; (2) value fusion; (3) majority decision fusion, and (4) optimal decision fusion. In all these cases, we demonstrate that our proposed control theoretic deployment approach is able to significantly outperform previously proposed algorithms. The second problem considered in this dissertation is the “self healing” problem in which we ask the following question: After the failure of a number of sensors, how can one reconfigure the DSN such that the performance degradation due to sensor loss is minimized? Prior efforts in tackling the self healing problem typically rely on assumptions that don’t accurately capture the behavior of practical sensors/networks and focus on minimizing performance degradation at a local area of the network instead of considering overall performance of the DSN. In this work, we propose two self healing strategies the first approach relies on adjusting decision thresholds at the fusion center. The second approach involves sensor redeployment based on our control theoretic deployment framework. Simulation results illustrate that the proposed algorithms are effective in alleviating the performance degradation due to sensor loss.

Active vibration control is a widely implemented method for the helicopter vibration control. Due to the significant progress in microelectronics, this technique outperforms the traditional passive control technique due to weight penalty and lack of adaptability for the changing flight conditions. In this thesis, an optimal controller is designed to attenuate the rotor blade vibration. The mathematical model of the triply coupled vibration of the rotating cantilever beam is used to develop the state-space model of an isolated rotor blade. The required natural frequencies are determined by the modified Galerkin method and only the principal aerodynamic forces acting on the structure are considered to obtain the elements of the input matrix. A linear quadratic regulator is designed to achieve the vibration reduction at the optimum level and the controller is tuned for the hovering and forward flight with different advance ratios.

Small satellites are becoming increasingly important to the aerospace industry mainly due to their significantly reduced development and launch cost as well as shorter development time frames. In order to meet the requirements imposed by critically limited resources of very small satellites, e.g. picosatellites, innovative approaches have to be taken in the design of effective subsystem technologies. This thesis presents the design of an active attitude determination and control system for flight testing on-board the picosatellite 'Compass-1' of the University of Applied Sciences Aachen, Germany. The spacecraft of the CubeSat class with a net spacecraft mass of only 1kg uses magnetic coils as the only means of actuation in order to satisfy operational requirements imposed by its imagery payload placed on a circular and polar Low Earth Orbit. The control system is capable of autonomously dissipating the tumbling rates of the spacecraft after launch interface separ ation and aligning the boresight of the payload into the desired nadir direction within a pointing error of approximately 10°. This nadir-pointing control is achieved by a full-state feedback Linear Quadratic Regulator which drives the attitude quaternion and their respective rates of change into the desired reference. The state of the spacecraft is determined by a static statistical QUEST attitude estimator processing readings of a three-axis magnetometer and a set of five sun sensors. Linear Floquet theory is applied to quantify the stability of the controller and a non-linear dynamics simulation is used to confirm that the attitude asymptotically converges to the reference in the absence of environmental disturbances. In the presence of disturbances the system under control suffers from fundamental underactuaction typical for purely magnetic attitude control but maintains satisfactory alignment accuracies within operational boundaries.

As inverter-based distributed energy resources (DERs) such as photovoltaic (PV) and battery energy storage system (BESS) penetrate within the distribution system. New challenges regarding how to utilize these devices to improve power quality arises. Before, PV systems were required to disconnect from the grid during a large disturbance, but now smart inverters are required to have dynamically controlled functions that allows them to remain connected to the grid. Monitoring power flow at the point of common coupling is one of the many functions the controller should perform. Smart inverters can inject active power to pick up critical load or inject reactive power to regulate voltage within the electric grid. In this context, this thesis focuses on a high level and local control design that incorporates DERs. Different controllers are implemented to stabilize the microgrid in an Islanding and resiliency mode. The microgrid can be used as a resiliency source when the distribution is unavailable. An average model in the D-Q frame is calculated to analyze the inherent dynamics of the current controller for the point of common coupling (PCC). The space vector approach is applied to design the voltage and frequency controller. Secondly, using inverters for Volt/VAR control (VVC) can provide a faster response for voltage regulation than traditional voltage regulation devices. Another objective of this research is to demonstrate how smart inverters and capacitor banks in the system can be used to eliminate the voltage deviation. A mixed-integer quadratic problem (MIQP) is formulated to determine the amount of reactive power that should be injected or absorbed at the appropriate nodes by inverter. The Big M method is used to address the nonconvex problem. This contribution can be used by distribution operators to minimize the voltage deviation in the system. / Master of Science / Reliable power supply from the electric grid is an essential part of modern life. This critical infrastructure can be vulnerable to cascading failures or natural disasters. A solution to improve power systems resilience can be through microgrids. A microgrid is a small network of interconnected loads and distributed energy resources (DERs) such as microturbines, wind power, solar power, or traditional internal combustion engines. A microgrid can operate being connected or disconnected from the grid. This research emphases on the potentially use of a Microgrid as a resiliency source during grid restoration to pick up critical load. In this research, controllers are designed to pick up critical loads (i.e hospitals, street lights and military bases) from the distribution system in case the electric grid is unavailable. This case study includes the design of a Microgrid and it is being tested for its feasibility in an actual integration with the electric grid. Once the grid is restored the synchronization between the microgrid and electric must be conducted. Synchronization is a crucial task. An abnormal synchronization can cause a disturbance in the system, damage equipment, and overall lead to additional system outages. This thesis develops various controllers to conduct proper synchronization. Interconnecting inverter-based distributed energy resources (DERs) such as photovoltaic and battery storage within the distribution system can use the electronic devices to improve power quality. This research focuses on using these devices to improve the voltage profile within the distribution system and the frequency within the Microgrid.

We study the behavior of numerical stabilization schemes in the context of linear quadratic regulator (LQR) control problems for convection diffusion equations. The motivation for this effort comes from the observation that when linearization is applied to fluid flow control problems the resulting equations have the form of a convection diffusion equation. This effort is focused on the specific problem of computing the feedback functional gains that are the kernels of the feedback operators defined by solutions of operator Riccati equations. We develop a stabilization scheme based on the Galerkin Least Squares (GLS) method and compare this scheme to the standard Galerkin finite element method. We use cubic B-splines in order to keep the higher order terms that occur in GLS formulation. We conduct a careful numerical investigation into the convergence and accuracy of the functional gains computed using stabilization. We also conduct numerical studies of the role that the stabilization parameter plays in this convergence. Overall, we discovered that stabilization produces much better approximations to the functional gains on coarse meshes than the unstabilized method and that adjustments in the stabilization parameter greatly effects the accuracy and convergence rates. We discovered that the optimal stabilization parameter for simulation and steady state analysis is not necessarily optimal for solving the Riccati equation that defines the functional gains. Finally, we suggest that the stabilized GLS method might provide good initial values for iterative schemes on coarse meshes. / Ph. D.

No description available.

In this thesis, analysis and comparison of different attitude control systems of a satelliteusing different reaction wheel configurations were investigated. Three different reactionwheel configurations (e.g. tetrahedron configuration, pyramid configuration, standardorthogonal 3-wheel configuration) and three control algorithms (Linear Quadratic Regulator,Sliding Mode, Integrator Backstepping) were analyzed and compared in terms of settlingtimes, power consumptions and actuator failure robustness. / <p>Validerat; 20121205 (global_studentproject_submitter)</p>

Semitrailer trucks are widely used for transportation of goods in Sweden and around the world. Given their usefulness, and since they require specialized drivers, there is an increased need to automate the operation of these vehicles. In particular, reversing these vehicles is considered a challenging maneuver, mainly because of the jackknifing effect. To tackle this challenge, this thesis investigates path following for reversing single-joint semitrailer trucks, by comparing two path-following controllers, corresponding to a Linear Quadratic Regulator (LQR) and a Model Predictive Control (MPC), respectively. Both controllers receive kinematically feasible reference trajectories from a path planner (which is part of another thesis work), which makes it possible to avoid jackknifing as long as the reference joint angle between the trailer and the truck is closely followed. Moreover, they use a linearized and discretized 1-trailer kinematic model, defined in terms of the reference tracking errors for the truck as states. To evaluate the performance of the controllers, a Python simulation is implemented using the 1-trailer kinematic model. Using this simulation, the controllers are compared using metrics related to the reference tracking errors along the generated path and the controller execution time. The results show that the LQR and the MPC controllers perform similarly for most cases. Even though there are certain cases where the MPC outperforms the LQR, the execution time of the MPC is at least one order of magnitude slower, which makes the LQR an attractive solution for practical implementations, as long as certain assumptions (small initial deviations, reliable measurements) are ensured. As such, an LQR controller might be preferred by the industry because, while the performance from both controllers is similar, it can be considered a more efficient controller. / Lastbilar med olika släpvagnskombinationer används ofta för godstransporter i Sverige och runt om i världen. Med tanke på deras användbarhet och eftersom de kräver specialiserade förare finns det ett ökat behov av att automatisera driften av dessa fordon. I synnerhet anses backning av dessa fordon vara en utmanande manöver, främst på grund av jackknifseffekten. För att lösa detta problem undersöker denna rapport vägföljande för backande lastbilar med släp genom att jämföra två olika vägföljande styrenheter: Linear Quadtratic Regulator (LQR) och Model Predictive Control (MPC). Båda styrenheterna får kinematiskt genomförbara referensbanor från en vägplanerare (som är en del av en annan uppsats), vilket gör det möjligt att undvika jackknipning så länge referensvinkeln mellan släpet och lastbilen följs noggrant. Dessutom använder de en linjäriserad och diskretiserad kinematisk modell med en lastbil, definierad i termer av lastbilens referensspårningsfel som tillstånd. För att utvärdera kontrollernas prestanda implementeras en Python-simulering med den kinematisk modell med en lastbil. Med denna simulering jämförs de två styrenheterna med mått relaterade till referensspårningsfelen längs den generarade vägen och styrenheternas exekveringstid. Resultaten visar att LQR och MPCpresterar likadant i de flesta fallen. Även om det finns vissa fall där MPC överträffar LQR, är exekveringstiden för MPC åtminstone en storleksordning långsammare, vilket gör LQR till en attraktiv lösning för praktiska implementeringar, så länge som vissa antaganden (små initiala avvikelser, pålitliga mått) säkerställs. Som sådan kan en LQR-styrenhet föredras av industrin, för även om prestandan från båda styrenheterna är lika, kan den betraktas som en enklare styrenhet.