The objective of this dissertation is to provide a framework for constructing a transitional behavior, connecting any two trajectories from a set with a particular characteristic, in such a way that the transition is as inconspicuous as possible. By this we mean that the connection is such that the characteristic behavior persists during the transition. These special classes include stationary solutions, limit cycles etc. We call this framework the Gluskabi raccordation. This problem is motivated from physical applications where it is often desired to steer a system from one stationary solution or periodic orbit to another in a ̒smooth̕ way. Examples include motion control in robotics, chemical process control and quasi-stationary processes in thermodynamics, etc. Before discussing the Gluskabi raccordations of periodic behaviors, we first study several periodic phenomena. Specifically, we study the self- propulsion of a number of legless, toy creatures based on differential friction under periodic excitations. This friction model is based on viscous friction which is predominant in a wet environment. We investigate the effects of periodic and optimal periodic control on locomotion. Subsequently, we consider a control problem of a stochastic system, under the basic constraint that the feedback control signal and the observations from the system cannot use the communication channel simultaneously. Hence, two modes of operation result: an observation mode and a control mode. We seek an optimal periodic regime in a statistical steady state by switching between the observation and the control mode. For this, the duty cycle and the optimal gains for the controller and observer in either mode are determined. We then investigate the simplest special case of the Gluskabi raccordation, namely the quasi-stationary optimal control problem. This forces us to revisit the classical terminal controller. We analyze the performance index as the control horizon increases to infinity. This problem gives a good example where the limiting operation and integration do not commute. Such a misinterpretation can lead to an apparent paradox. We use symmetrical components (the parity operator) to shed light on the correct solution. The main part of thesis is the Gluskabi raccordation problem. We first use several simple examples to introduce the general framework. We then consider the signal Gluskabi raccordation or the Gluskabi raccordation without a dynamical system. Specifically, we present the quasi-periodic raccordation where we seek the maximally ̒smooth̕ transitions between two periodic signals. We provide two methods, the direct and indirect method, to construct these transitions. Detailed algorithms for generating the raccordations based on the direct method are also provided. Next, we extend the signal Gluskabi raccordation to the dynamic case by considering the dynamical system as a hard constraint. The behavioral modeling of dynamical system pioneered by Willems provides the right language for this generalization. All algorithms of the signal Gluskabi raccordation are extended accordingly to produce these ̒smooth̕ transition behaviors.

Bei der Kristallzüchtung insbesondere von Halbleitern hat die Form des freien Randes (dem Interface zwischen fester und flüssiger Phase) einen starken Einfluss auf die Qualität des Kristalls. Die Dissertation befasst sich mit der Optimalsteuerung der Form und des Verlaufs des freien Randes. Als Vorlage für die in der Arbeit betrachteten Modellkonfigurationen dient das VGF-Verfahren (Vertical Gradient Freeze). Der Erstarrungsprozess wird durch ein Zweiphasen-Stefan-Problem mit durch Konvektion und Lorentzkräfte getriebener Strömung beschrieben. Der freie Rand wird als Graph formuliert. Das Kontrollziel besteht in der Ansteuerung eines gewünschten Verlaufs des freien Randes. Als Kontrollgrößen dient die Temperatur auf der Wand des Schmelztiegels und/oder wandnahe oder verteilte Lorentzkräfte. Das Kontrollziel wird durch Minimierung eines geeigneten Kosten-Funktionals erreicht. Das daraus resultierende Minimierungsproblem wird mit einem Adjungierten-Ansatz gelöst. Anhand numerischer Experimente mit Aluminium und Gallium-Arsenid Schmelzen wird gezeigt, das das vorgestellte Verfahren gut funktioniert.

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.

Liquid desiccant air conditioning (LDAC) has received much attention in recent years. This is mainly because LDAC systems are able to control latent loads in a more energy efficient way than conventional air conditioning systems. Although many research studies have been conducted on LDAC technologies, the following gaps in the scientific literature are addressed in this thesis: (1) carryover of desiccant droplets in air streams, (2) direct comparisons between different configurations of LDAC systems, (3) fundamentals of capacity matching in heat-pump LDAC systems, (4) optimal-control strategies for heat-pump LDAC systems, and (5) importance of transients in evaluating the performance of a LDAC system. Items (1) to (4) are addressed using TRNSYS simulations, and item (5) is addressed using data collected from a field test. The use of liquid-to-air membrane energy exchangers (LAMEEs) as dehumidifiers and regenerators in LDAC systems eliminate the desiccant droplets carryover problem in air streams. This is because LAMEE separate the air and solution streams using semi-permeable membranes, which allow the transfer of heat and moisture but do not allow the transfer of the liquid desiccant. A preliminary configuration for a membrane LDAC system, which uses LAMEEs as the dehumidifier and regenerator, is proposed and investigated under fixed operating conditions in this thesis. The influences of key design and operating parameters on the heat and mass transfer performances of the membrane LDAC system are evaluated. Results show that the membrane LDAC technology is able to effectively remove latent loads in applications that the humidity to be controlled. A comprehensive evaluation is conducted in this thesis for the thermal, economic and environmental performances of several configurations of membrane LDAC systems. The solution cooling load is covered using a cooling heat pump in all systems studied, while the solution heating load is covered using one of the following five different heating systems: (1) a gas boiler, (2) a heating heat pump, (3) a solar thermal system with gas boiler backup, (4) a solar thermal system with heat pump backup, and (5) the condenser of the solution cooling heating pump. Each of the membrane LDAC systems studied is evaluated with/without an energy recovery ventilator (ERV) installed in the air handling system. The influence of operating the ERV under balanced/unbalanced operating conditions is studied. It is found that the most economic membrane LDAC system is the one which uses the evaporator and condenser of the same heat pump to cover the solution cooling and heating loads, respectively (i.e. heat-pump membrane LDAC system). No clear guidance was found in the literature for sizing the evaporator and condenser in a heat-pump LDAC system to simultaneously meet the solution cooling and heating loads. When the heating and cooling provided by the heat pump exactly match the heating and cooling requirements of the solution, the system is “capacity matched”. A parametric study is conducted on a heat-pump membrane LDAC system to identify the influence of key operating and design parameters on achieving capacity matching. It is concluded that the solution inlet temperatures to the dehumidifier and regenerator are the most influential parameters on the moisture removal rate, capacity matching and coefficient of performance (COP). Three control strategies are developed for heat-pump membrane LDAC systems, where these strategies meet the latent loads and achieve one of the following three objectives: (1) meet the sensible loads, (2) achieve capacity matching, or (3) optimize the COP. Results show that the COP of a heat-pump LDAC system can be doubled by selecting the right combination of solution inlet temperatures to the regenerator and dehumidifier. The importance of transients in evaluating the performance of a LDAC system is addressed in the thesis using a data collected from a field test on a solar LDAC system. It is found that the sensible, latent and total cooling energy, and the total primary energy consumption of the LDAC system are changed by less than 10% during an entire test day when transients are considered. Thus, it can be concluded that steady-state models are reliable to evaluate the energy performances of LDAC systems.

No description available.

This thesis addresses flight control of the perch landing maneuver for micro-aerial vehicles. A longitudinal flight model is constructed for a pigeon-sized aircraft. In addition to a standard elevator control surface, wing-rotation also considered as a non-standard actuator for increasing low-speed aerodynamic braking. Optimal state and control trajectories for the perch landing maneuver are computed using commercial software. A neighboring optimal control law is then developed and implemented in a set of flight simulations. Simulations are run with both a quasisteady and an unsteady aerodynamic model. The effectiveness of wing rotation and of the neighboring optimal control law is discussed, as is the importance of unsteady aerodynamics during the maneuver. Wing rotation was found to be minimally effective in this case, but it showed potential to be more effective in further research. The unsteady aerodynamic model has significant influence over the success or failure of the maneuver.

This thesis addresses optimal control problems with elasticity for large deformations. A hyperelastic model with a polyconvex energy density is employed to describe the elastic behavior of a body. The two approaches to derive the nonlinear partial differential equation, a balance of forces and an energy minimization, are compared. Besides the conventional volume and boundary loads, two novel internal loads are presented. Furthermore, curvilinear coordinates and a hierarchical plate model can be incorporated into the formulation of the elastic forward problem. The forward problem can be solved with Newton\\\'s method, though a globalization technique should be used to avoid divergence of Newton\\\'s method. The repeated solution of the Newton system is done by a CG or MinRes method with a multigrid V-cycle as a preconditioner. The optimal control problem consists of the displacement (as the state) and a load (as the control). Besides the standard tracking-type objective, alternative objective functionals are presented for problems where a reasonable desired state cannot be provided. Two methods are proposed to solve the optimal control problem: an all-at-once approach by a Lagrange-Newton method and a reduced formulation by a quasi-Newton method with an inverse limited-memory BFGS update. The algorithms for the solution of the forward problem and the optimal control problem are implemented in the finite-element software FEniCS, with the geometrical multigrid extension FMG. Numerical experiments are performed to demonstrate the mesh independence of the algorithms and both optimization methods.

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Gr¨unwald & Dawid, 2003). We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.

There is a considerable scope for improving the temperature control of exothermic processes. In this thesis, a sub-optimal control strategy is developed through utilizing the dynamic, simulation tool. This scheme is built around easily obtained knowledge of the system and still retains flexibility. It can be applied to both exothermic batch and fed-batch processes. It consists of servo and regulatory modes, where a Generalized Predictive Controller (GPC) was used to provide self-tuning facilities. The methods outlined allow for limited thermal runaway whilst keeping some spare cooling capacity to ensure that operation at constraints are not violated. A special feature of the method proposed is that switching temperatures and temperature profiles can be readily found from plant trials whilst the addition rate profile Is capable of fairly straightforward computation. The work shows that It is unnecessary to demand stability for the whole of the exothermic reaction cycle, permitting a small runaway has resulted in a fast temperature response within the given safety margin. The Idea was employed for an exothermic single Irreversible reaction and also to a set of complex reactions. Both are carried out in a vessel with a heating/cooling coil. Two constraints are Imposed; (1) limited heat transfer area, and (11) a maximum allowable reaction temperature Tmax. The non-minimum phase problem can be considered as one of the difficulties in managing exothermic fed-batch process when cold reactant Is added to vessel at the maximum operating temperature. The control system coped with this within limits, a not unexpected result. In all cases, the new strategy out-performed the conventional controller and produced smoother variations in the manipulated variable. The simulation results showed that batch to batch variations and disturbances In cooling were successfully handled. GPC worked well but can be susceptible to measurement noise.

<p>This dissertation presents a novel distributed optimal control (DOC) problem formulation that is applicable to multiscale dynamical systems comprised of numerous interacting systems, or agents, that together give rise to coherent macroscopic behaviors, or coarse dynamics, that can be modeled by partial differential equations (PDEs) on larger spatial and time scales. The DOC methodology seeks to obtain optimal agent state and control trajectories by representing the system's performance as an integral cost function of the macroscopic state, which is optimized subject to the agents' dynamics. The macroscopic state is identified as a time-varying probability density function to which the states of the individual agents can be mapped via a restriction operator. Optimality conditions for the DOC problem are derived analytically, and the optimal trajectories of the macroscopic state and control are computed using direct and indirect optimization algorithms. Feedback microscopic control laws are then derived from the optimal macroscopic description using a potential function approach.</p><p>The DOC approach is demonstrated numerically through benchmark multi-agent trajectory optimization problems, where large systems of agents were given the objectives of traveling to goal state distributions, avoiding obstacles, maintaining formations, and minimizing energy consumption through control. Comparisons are provided between the direct and indirect optimization techniques, as well as existing methods from the literature, and a computational complexity analysis is presented. The methodology is also applied to a track coverage optimization problem for the control of distributed networks of mobile omnidirectional sensors, where the sensors move to maximize the probability of track detection of a known distribution of mobile targets traversing a region of interest (ROI). Through extensive simulations, DOC is shown to outperform several existing sensor deployment and control strategies. Furthermore, the computation required by the DOC algorithm is proven to be far reduced compared to that of classical, direct optimal control algorithms.</p> / Dissertation