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Small aerial vehicles such as quad-rotors have been widely used commercially, for research and for hobby for the last decade with use still growing. The high interest is mainly due to the vehicles being small, simple, cheap and versatile. Among rigid body dynamics fast dynamics exist cohering to motors and other fast actuators. A linear quadratic control design technique is here investigated. The design technique suggests that the linear quadratic controller can be designed with penalties on the slow states only. The fast dynamics are modeled but the states are not penalised in the linear quadratic design. The design technique is here applied and evaluated. The results show that this in several cases is a suitable design technique for linear quadratic control design. MATLAB and Simulink have been widely used for design and implementation of control systems. With additional toolboxes these control systems can be compiled to and run on remote computers. Small, lightweight computers with high computational capacity are now easily accessible. In this thesis an avionics solution based on a small, powerful computer is presented. Simulink models can be compiled and transferred to the computer from the Simulink environment. The result is a user friendly way of rapid prototyping and evaluation of control systems.

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.

In this thesis, we give a mathematical background of solving a linear quadratic control problem for the heat equation, which involves noise on the boundary, in a concise way. We use the semigroup approach for the solvability of the problem. To obtain optimal controls, we use optimization techniques for convex functionals. Finally we give a feedback form for the optimal control. In order to enhance understanding of linear quadratic problem, we first present the methods in deterministic cases and then extend to noisy systems.

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.

While stochastic optimal control, together with associate formulations like Reinforcement Learning, provides a formal approach to, amongst other, motor control, it remains computationally challenging for most practical problems. This thesis is concerned with the study of relations between stochastic optimal control and probabilistic inference. Such dualities { exempli ed by the classical Kalman Duality between the Linear-Quadratic-Gaussian control problem and the filtering problem in Linear-Gaussian dynamical systems { make it possible to exploit advances made within the separate fields. In this context, the emphasis in this work lies with utilisation of approximate inference methods for the control problem. Rather then concentrating on special cases which yield analytical inference problems, we propose a novel interpretation of stochastic optimal control in the general case in terms of minimisation of certain Kullback-Leibler divergences. Although these minimisations remain analytically intractable, we show that natural relaxations of the exact dual lead to new practical approaches. We introduce two particular general iterative methods ψ-Learning, which has global convergence guarantees and provides a unifying perspective on several previously proposed algorithms, and Posterior Policy Iteration, which allows direct application of inference methods. From these, practical algorithms for Reinforcement Learning, based on a Monte Carlo approximation to ψ-Learning, and model based stochastic optimal control, using a variational approximation of posterior policy iteration, are derived. In order to overcome the inherent limitations of parametric variational approximations, we furthermore introduce a new approach for none parametric approximate stochastic optimal control based on a reproducing kernel Hilbert space embedding of the control problem. Finally, we address the general problem of temporal optimisation, i.e., joint optimisation of controls and temporal aspects, e.g., duration, of the task. Specifically, we introduce a formulation of temporal optimisation based on a generalised form of the finite horizon problem. Importantly, we show that the generalised problem has a dual finite horizon problem of the standard form, thus bringing temporal optimisation within the reach of most commonly used algorithms. Throughout, problems from the area of motor control of robotic systems are used to evaluate the proposed methods and demonstrate their practical utility.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / A cyber physical system (CPS) is an intelligent integration of computation and communication infrastructure for monitoring and/or control of an underlying physical system. In this dissertation, we consider a specific class of CPS architectures where state of the system is spatially distributed in physical space. Examples that fit this category of CPS include, smart distribution gird, smart highway/transportation network etc. We study state estimation and control process in such systems where, (1) multiple sensors and actuators are arbitrarily deployed to jointly sense and control the system; (2) sensors directly communicate their observations to a central estimation and control unit (ECU) over communication links; and, (3) the ECU, on computing the control action, communicates control actions to actuators over communication links. Since communication links are susceptible to random failures, the overall estimation and control process is subjected to: (1) partial observation updates in estimation process; and (2) partial actuator actions in control process. We analyze stochastic stability of estimation and control process, in this scenario by establishing the conditions under which estimation accuracy and deviation from desired state trajectory is bounded. Our key contribution is the derivation of a new fundamental result on bounds for critical probabilities of individual communication link failure to maintain stability of overall system. The overall analysis illustrates that there is trade-off between stability of estimation and control process and quality of underlying communication network. In order to demonstrate practical implication of our work, we also present a case study in smart distribution grid as a system example of spatially distributed CPSs. Voltage/VAR support via distributed generators is studied in a stochastic nonlinear control framework.

Uma câmera pan-tilt é capaz de se movimentar em torno de dois eixos de rotação (pan e tilt), permitindo que sua lente possa ser apontada para um ponto qualquer no espaço. Uma aplicação possível dessa câmera é mantê-la apontada para um determinado alvo em movimento, através de posicionamentos angulares pan e tilt adequados. Este trabalho apresenta uma técnica de controle servo visual, em que, inicialmente, as imagens capturadas pela câmera são utilizadas para determinar a posição do alvo. Em seguida, calculam-se as rotações necessárias para manter a projeção do alvo no centro da imagem, em um sistema em tempo real e malha fechada. A técnica de rastreamento visual desenvolvida se baseia em comparação de uma região de referência, utilizando a soma dos quadrados das diferenças (SSD) como critério de correspondência. Sobre essa técnica, é adicionada uma extensão baseada no princípio de estimação incremental e, em seguida, o algoritmo é mais uma vez modificado através do princípio de estimação em multiresolução. Para cada uma das três configurações, são realizados testes para comparar suas performances. O sistema é modelado através do princípio de fluxo óptico e dois controladores são apresentados para realimentar o sistema: um proporcional integral (PI) e um proporcional com estimação de perturbações externas através de um filtro de Kalman (LQG). Ambos são calculados utilizando um critério linear quadrático e os desempenhos deles também são analisados comparativamente. / A pan-tilt camera can move around two rotational axes (pan and tilt), allowing its lens to be pointed to any point in space. A possible application of the camera is to keep it pointed to a certain moving target, through appropriate angular pan-tilt positioning. This work presents a visual servoing technique, which uses first the images captured by the camera to determinate the target position. Then the method calculates the proper rotations to keep the target position in image center, establishing a real-time and closed-loop system. The developed visual tracking technique is based on template region matching, and makes use of the sum of squared differences (SSD) as similarity criterion. An extension based on incremental estimation principle is added to the technique, and then the algorithm is modified again by multiresolution estimation method. Experimental results allow a performance comparison between the three configurations. The system is modeled through optical flow principle and this work presents two controllers to accomplish the system feedback: a proportional integral (PI) and a proportional with external disturbances estimation by a Kalman filter (LQG). Both are determined using a linear quadratic method and their performances are also analyzed comparatively.

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.