Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Trajectory Optimization of a Tethered Underwater Kite(2021) Alvarez Tiburcio, Miguel; Fathy, Hosam; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses the challenge of optimizing the motion trajectory of a tethered marine hydrokinetic energy harvesting kite in order to maximize its average electric power output. The dissertation focuses specifically on the “pumping” kite configuration, where the kite is periodically reeled out from a floating base station at high tension, then reeled in at low tension. This work is motivated by the significant potential for sustainable electricity generation from marine currents such as the Gulf Stream. Tethered systems can increase their energy harvesting potential significantly through cross-current motion. Such motion increases apparent flow speed, which is valuable because the instantaneous maximum power that can be harvested is proportional to the cube of this apparent speed. This makes it possible for tethered systems to achieve potentially very attractive power densities and levelized costs of electricity compared to stationary turbines. However, this also necessitates the use of trajectory optimization and active control in order to eke out the maximum energy harvesting capabilities of these systems. The problem of optimizing the trajectories of these kites is highly non-linear and thus challenging to solve. In this dissertation we make key simplifications to both the modeling and the structure of the optimal solution which allows us to learn valuable insights in the nature of the power maximizing trajectory. We first do this analysis to maximize the average mechanical power of the kite, then we expand it to take into account system losses. Finally, we design and fabricate an experimental setup to both parametrize our model and validate our trajectories. In summary, the goal of this research is to furnish model-based algorithms for the online optimal flight control of a tethered marine hydrokinetic system. The intellectual merit of this work stems from the degree to which it will tackle the difficulty of solving this co-optimization problem taking into account overall system efficiency and the full range of possible system motion trajectories. From a broader societal perspective, this work represents a step towards experimentally validating the potential of pumped underwater kite systems to serve as renewable energy harvesters in promising environments such as the Gulf Stream.Item INTERPOLATION OF RIGID-BODY MOTION AND GALERKIN METHODS FOR FLEXIBLE MULTIBODY DYNAMICS(2019) Han, Shilei; Bauchau, Olivier A.; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Traditionally, flexible multibody dynamics problems are formulated as initial value problems: initial states of the system are given and solving for the equations of motion yields the dynamic response. Many practical problems, however, are boundary rather than initial value problems; two-point and periodic boundary problems, in particular, are quite common. For instance, the trajectory optimization of robotic arms and spacecrafts is formulated as a two-point boundary value problem; determination of the periodic dynamic response of helicopter and wind turbine blades is formulated as a periodic boundary value problem; the analysis of the stability of these periodic solutions is another important of problem. The objective of this thesis is to develop a unified solution procedure for both initial and boundary value problems. Galerkin methods provide a suitable framework for the development of such solvers. Galerkin methods require interpolation schemes that approximate the unknown rigid-body motion fields. Novel interpolation schemes for rigid-body motions are proposed based on minimization of eighted distance measures of rigid-body motions. Based on the proposed interpolation schemes, a unified continuous/discontinuous Galerkin solver is developed for the formulation of geometrically exact beams, for the determination of solutions of initial and periodic boundary value problems, for the stability analysis of periodic solutions, and for the optimal control/optimization problems of flexible multibody systems.Item Optimal Control of Hybrid Systems, with Application to Vehicle Dynamics(2011) Kefauver, Kevin; Levine, William; Balachandran, Balakumar; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Hybrid dynamical systems are common throughout the physical and computer world, and they consist of dynamical systems that contain both continuous time and discrete time dynamics. Examples of this type of system include thermostat controlled systems, multi-geared transmission based systems, and embedded computer systems. Sometimes, complicated non-linear continuous time systems can be simplified by breaking them up into a set of less complicated continuous systems connected through discrete interactions (referred to as system hybridization). One example is modeling of vehicle dynamics with complicated tire-to-ground interaction by using a tire slipping or no slip model. When the hybrid system is to be a controlled dynamical system, a limited number of tools exist in the literature to synthesize feedback control solutions in an optimal way. The purpose of this dissertation is to develop necessary and sufficient conditions for finding optimal feedback control solutions for a class of hybrid problems that applies to a variety of engineering problems. The necessary and sufficient conditions are developed by decomposing the hybrid problem into a series of non-hybrid optimal feedback control problems that are coupled together with the appropriate boundary conditions. The conditions are developed by using a method similar to Bellman's Dynamic Programming Principle. The solution for the non-hybrid optimal control problem that contains the final state is found and then propagated backwards in time until the solution is generated for every node of the hybrid problem. In order to demonstrate the application of the necessary and sufficient conditions, two hybrid optimal control problems are analyzed. The first problem is a theoretical problem that demonstrates the complexity associated with hybrid systems and the application of the hybrid analysis tools. Through the control problem, a solution is found for the feedback control that minimizes the time to the origin problem for a hybrid system that is a combination of two standard optimal control problems found in the literature; the double integrator system and a harmonic oscillator. Through the second problem, optimal feedback control is found for the drag racing and hot-rodding control problems for any initial reachable state of the system and a hybrid model of a vehicle system with tire-to-ground interaction.Item Optimal Control with Information Pattern Constraints(2011) Sabau, Serban; Martins, Nuno C.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite the abundance of available literature that starts with the seminal paper of Wang and Davison almost forty years ago, when dealing with the problem of decentralized control for linear dynamical systems, one faces a surprising lack of general design methods, implementable via computationally tractable algorithms. This is mainly due to the fact that for decentralized control configurations, the classical control theoretical framework falls short in providing a systematic analysis of the stabilization problem, let alone cope with additional optimality criteria. Recently, a significant leap occurred through the theoretical machinery developed in Rotkowitz and Lall, IEEE-TAC, vol. 51, 2006, pp. 274-286 which unifies and consolidates many previous results, pinpoints certain tractable decentralized control structures, and outlines the most general known class of convex problems in decentralized control. The decentralized setting is modeled via the structured sparsity constraints paradigm, which proves to be a simple and effective way to formalize many decentralized configurations where the controller feature a given sparsity pattern. Rotkowitz and Lall propose a computationally tractable algorithm for the design of H2 optimal, decentralized controllers for linear and time invariant systems, provided that the plant is strongly stabilizable. The method is built on the assumption that the sparsity constraints imposed on the controller satisfy a certain condition (named quadratic invariance) with respect to the plant and that some decentralized, strongly stablizable, stabilizing controller is available beforehand. For this class of decentralized feedback configurations modeled via sparsity constraints, so called quadratically invariant, we provided complete solutions to several open problems. Firstly, the strong stabilizability assumption was removed via the so called coordinate free parametrization of all, sparsity constrained controllers. Next we have addressed the unsolved problem of stabilizability/stabilization via sparse controllers, using a particular form of the celebrated Youla parametrization. Finally, a new result related to the optimal disturbance attenuation problem in the presence of stable plant perturbations is presented. This result is also valid for quadratically invariant, decentralized feedback configurations. Each result provides a computational, numerically tractable algorithm which is meaningful in the synthesis of sparsity constrained optimal controllers.Item Adaptive Magnetorheological Sliding Seat System for Ground Vehicles(2011) Mao, Min; Wereley, Norman M.; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Magnetorheological (MR) fluids (MRFs) are smart fluids that have reversible field dependent rheological properties that can change rapidly (typically 5 - 10 ms time constant). Such an MRF can be changed from a free flowing fluid into a semi-solid when exposed to a magnetic field. The rapid, reversible, and continuous field dependent variation in rheological properties can be exploited in an MRF-based damper or energy absorber to provide adaptive vibration and shock mitigation capabilities to varying payloads, vibration spectra, and shock pulses, as well as other environmental factors. Electronically controlled electromagnetic coils are typically used to activate the MR effect and tune the damping force so that feedback control implementation is practical and realizable. MR devices have been demonstrated as successful solutions in semi-active systems combining advantages of both passive and active systems for applications where piston velocities are relatively low (typically < 1 m/s), such as seismic mitigation, or vibration isolation. Recently strong interests have focused on employing magnetorheological energy absorbers (MREAs) for high speed impact loads, such as in helicopter cockpit seats for occupant protection in a vertical crash landing. This work presents another novel application of MREAs in this new trend - an adaptive magnetorheological sliding seat (AMSS) system utilizing controllable MREAs to mitigate impact load imparted to the occupant for a ground vehicle in the event of a low speed frontal impact (up to 15 mph). To accomplish this, a non-linear analytical MREA model based on the Bingham-plastic model and including minor loss effects (denoted as the BPM model) is developed. A design strategy is proposed for MREAs under impact conditions. Using the BPM model, an MREA is designed, fabricated and drop tested up to piston velocities of 5 m/s. The measured data is used to validate the BPM model and the design strategy. The MREA design is then modified for use in the AMSS system and a prototype is built. The prototype MREA is drop tested and its performance, as well as the dynamic behavior in the time domain, is described by the BPM model. Next, theoretical analysis of the AMSS system with two proposed control algorithms is carried out using two modeling approaches: (1) a single-degree-of-freedom (SDOF) rigid occupant (RO) model treating the seat and the occupant as a single rigid mass, and (2) a multi-degree-of-freedom (MDOF) compliant occupant (CO) model interpreting the occupant as three lumped parts - head, torso and pelvis. A general MREA is assumed and characterized by the Bingham-plastic model in the system model. The two control algorithms, named the constant Bingham number or Bic control and the constant stroking force or Fc control, are constructed in such a way that the control objective - to bring the payload to rest while fully utilizing the available stroke - is achieved. Numerical simulations for both rigid and compliant occupant models with assumed system parameter values and a 20 g rectangular crash pulse for initial impact speeds of up to 7 m/s (15.7 mph) show that overall decelerations of the payload are significantly reduced using the AMSS compared to the case of a traditional fixed seat. To experimentally verify the theoretical analysis, a prototype AMSS system is built. The prototype seat system is sled tested in the passive mode (i.e. without control) for initial impact speeds of up to 5.6 m/s and for the 5th percentile female and the 95th percentile male. Using the test data, the CO model is shown to be able to adequately describe the dynamic behavior of the prototype seat system. Utilizing the CO model, the control algorithms for the prototype seat system are developed and a prototype controller is formulated using the DSPACE and SIMULINK real time control environments. The prototype seat system with controller integrated is sled tested for initial impact speeds of up to 5.6 m/s for the 5th female and 95th male (only the 95th male is tested for the Bic control). The results show that the controllers of both control algorithms successfully bring the seat to rest while fully utilizing the available stroke and the decelerations measured at the seat are substantially mitigated. The CO model is shown to be effective and a useful tool to predict the control inputs of the control algorithms. Thus, the feasibility and effectiveness of the proposed adaptive sliding seat system is theoretically and experimentally verified.Item Optimal Selection of Measurements and Manipulated Variables for Production Control(2008-10-14) Abi Assali, Wuendy; Mc Avoy, Thomas J; Zafiriou, Evanghelos; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The main objective in a chemical plant is to improve profit while assuring products meet required specifications and satisfy environmental and operational constraints. A sub-objective that directly affects profit (main objective) is to improve the control performance of key economic variables in the plant, such as production rate and quality. An optimal control-based approach is proposed to determine a set of measurements and manipulated variables (dominant variables) and to structure them to improve plant profitability. This approach is model-based, and it uses optimal control theory to find the dominant variables that affect economic variables in the plant. First, the measurements and manipulated variables that affect product flow and quality are identified. Then, a decentralized control structure is designed to pair these measurements with the manipulated variables. Finally, a model predictive control (MPC) is built on top of the resulting control structure. This is done to manipulate the set point of these loops in order to change the production rate and product quality. Another sub-objective that affects the profit in the plant is to improve the control of inerts. In general, the inventory of the inerts is controlled using a purge. A new methodology to optimally control inerts is presented. This methodology aims to reduce the losses that occur throughout the purge by solving an optimization problem to determine the maximum amount of inert that can be handled in the plant without having shut down of the plant due to inert accumulation. The methodology is successfully applied to the Tennessee Eastman Plant where the operating cost was reduced approximately 4%. This methodology solves an approximation to an optimal economic problem. First, it improves the control performance of key economic variables in the plant. Therefore, tighter control of these economic variables is achieved and the plant can be operated closer to operational constraints. Second, it minimizes purge which is a variable that generally causes significant costs in the plant. This approach is applied to the Tennessee Eastman and the Vinyl Acetate Processes. Results demonstrating the effectiveness of this method are presented and compared with the results from other authors.