Master of Science, The Ohio State University, 2022, Mechanical Engineering

An efficient simulation framework for co-optimization of design and control is fundamental in the development phase of hybrid electric vehicles to achieve the best system- level improvements of energy efficiency and emissions. Coordination schemes for co- optimization have been widely investigated in the literature, but only for a limited number and nature of design and control variables. In this study a decomposition-based coordination scheme capable to handle multi-time scale, time variant and time invariant (discrete and continuous) variables with ability to handle each sub-problem with different solver is not only demonstrated but also compared with simultaneous-based scheme in terms of optimality of the solution and computational performance. The two coordination schemes are used to co-optimize energy management strategy and components sizing for a series hybrid truck. In addition, multiple objectives are weighted in the cost function: fuel consumption, battery size, and tailpipe pollutant emissions. Results show that the simultaneous scheme is computationally less expensive for simple problems, but it becomes computationally inefficient with increasing problem complexity, with the additional drawback of not being able to handle integer-valued dynamic variables. On the other hand, the decomposition-based scheme can solve such problems, but with a more complex problem formulation. Results show that the decomposition-based scheme has not only 14% improvement in computational performance, but the optimality of the solution is also comparable with simultaneous-based scheme. Hence, as compared to the dynamic optimization, co-optimization yields up to 3.7% improvement in the average genset efficiency operation. Moreover, the fuel consumption for dynamic optimization was 2.5 kg which is reduced to 1.6 kg with co-optimization and was further reduced to 1.5 by adding engine on off control.

Committee: Qadeer Ahmed (Advisor); Shawn Midlam-Mohler (Committee Member); Manfredi Villani (Other) Subjects: Aerospace Engineering; Automotive Engineering; Electrical Engineering; Mechanical Engineering; Robotics

Master of Science, The Ohio State University, 0, Electrical and Computer Engineering

The transportation sector is making a transition from conventional engine vehicles to hybrid electric vehicles because of the environmental concerns like global warming. HEVs are a very lucrative option today because it helps reduce the usage of fossil fuels without much compromise on the range of the vehicle. This is because HEVs offer extra degrees of freedom to operate the vehicle in electric mode or engine mode or both. This calls for optimizing the powertrain of a HEV. As a part of this research work, we present a more realistic approach by considering a large state-space which engenders complex dynamics/ interactions between multiple sub-systems. A P2 parallel hybrid powertrain of a class 6 Pick-up & Delivery truck is considered as the case-study problem. This problem involves 13 states and 4 control levers. Some of these variables are discrete in nature and some are continuously varying with respect to time. Some have slow dynamics like temperature, while some have fast dynamics like battery state of charge which makes it a stiff system. Usage of LUTs, interpolations and conditional formulations exacerbate the complexity of the problem already considered. Optimization of all these variables together makes it very challenging for the solver hence, a novel three-step approach is presented and used to solve the case-study problem. This makes use of pseudo spectral method (PSC) for handling real-valued variables and for accurate state estimations and Dynamic programming (DP) for the optimization of integer-valued variables. We present three scenarios for the case-study problem where fuel consumption alone is minimized, emissions alone are minimized and, lastly a combination of both fuel and emissions are minimized. The computation time for this huge problem is only of the order of 50-80 minutes using the 3-step approach. The fuel minimization case has the least fuel and highest emissions, and vice versa for the emissions minimization case. The fuel & emissions pr (open full item for complete abstract)

Committee: Qadeer Ahmed (Advisor); Vadim Utkin (Committee Member) Subjects: Aerospace Engineering; Automotive Engineering; Electrical Engineering; Mechanical Engineering; Robotics

Master of Science in Civil Engineering, Cleveland State University, 2020, Washkewicz College of Engineering

Conventional design methods for combined footings comprise a series of iterations. Generally, this involves an initial guess for the dimensions which are evaluated as guided by the existing design code. This is then followed by several iterations to reduce the cost without any detriment to structural safety. In most cases, the result from the final iteration does not reflect the minimum cost design. This necessitates optimization models capable of establishing efficient and accurate designs within a short period, especially under several design variables. For this purpose, an optimization model for concentric loaded rectangular combined footings was developed in this research. The model was built in a general form and can perform optimization with different soil and material properties. The model encompasses an accurate objective function, subjected to the structural, geotechnical, and logical constraints to satisfy the requirements of the strength and serviceability limit states in accordance with ACI 318-11M specifications. The model works to find the minimal construction cost of the structure, adequate dimensions, and steel areas in different sections that correspond to that minimal cost. The model was developed using five solvers available within the MATLAB Global Optimization toolbox. Model capabilities were investigated by optimizing a case of concentric loaded rectangular combined footing with a known solution. The model capabilities were also assessed by testing the effect of using different material properties and varying site conditions on the resulting objective function. The optimization results showed identical results compared to the conventional design methodology. The results also showed the cost tends to decrease with the use of higher steel grades for all load variations. Moreover, there was no major effect for the concrete compressive strength in the range of 20 to 35 MPa on the value of the objective function. However, for higher concrete (open full item for complete abstract)

Committee: Josiah Owusu-Danquah (Committee Chair); Lutful Khan (Committee Member); Stephen Duffy (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, Case Western Reserve University, 2005, Systems and Control Engineering

This research addresses the planning of Gamma Knife radiotherapy, which is an alternative to treating a variety of brain abnormalities with surgery. The principal aim of this work is to develop an automated planning system that will make it simpler, less time consuming and hopefully more effective for the clinical personnel to develop treatment plans. Currently, treatment planning is a time-consuming task that involves an iterative process of shot selection, placement, and adjustment. Our goal is to replace the iterative part of the planning with an optimization-based real-time planner. Our strategy is to: (1) automate initial shot selection and placement using a combined process of skeletonization and bin-covering, (2) optimize the exposure time for each shot to improve the target coverage while minimizing toxicity to the surrounding tissues, and (3) fine-tune the shot configuration by adjusting shot locations, and adding or deleting shots to further improve the balance between target coverage and normal tissue toxicity. The efficiency and effectiveness of our approach is derived from (1) the use of skeletonization and bin-covering to provide good starting point for the development of the plan, (2) the easy-to-solve linear fractional program that explicitly accounts for the dual objectives of maximizing target coverage while minimizing toxicity, at the same time accounting for dose-renormalization and (3) the fine-tuning step that explicitly accounts for shot overlapping, dose renormalization, and target shape to make determining of hot spots and estimating the effects of shot movement, addition and/or deletion possible. The planning system has been implemented on a Windows-based platform and tested using clinical cases from the standard Gamma Knife treatment model as well as the automatic positioning system (APS) model. The planner consistently produces, in 1-2 minutes, plans with dose conformity compatible to manual plans normally created in 1-4 hours.

Committee: Vira Chankong (Advisor) Subjects: Engineering, System Science

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

Within this thesis document we focus on the application of Nonlinear Model Predictive Control (NMPC) onto an epidemic compartmental model. The compartmental model is a partial differential equation (PDE) based Susceptible Latent Infected Recovered (SLIR) epidemic model. This model serves as the basis of the NMPC. In order to generate the necessary parameters for initializing and training the use of constrained optimization, a single-objective Genetic Algorithm (GA), and LSTM (Long-Short-Term-Memory) deep learning were explored. The spatial domains considered for the SLIR epidemic model includes Hamilton County, Ohio as well as the entire state of Ohio, USA. With respect to Hamilton County, Ohio three different time periods were evaluated in which varied levels of infection relating to COVID-19 were observed. At the state wide level only one time period was consider. The NMPC considers two control schemes. The first being control applied uniformly across the spatial domain of interest. While the second focuses on applying the control in a spatially targeted manner to specific geographical areas based on observed higher levels of infection. The NMPC also employs a cost function comprising the infection spread density and the associated cost of applied control measures. The latter of which in turn representing socioeconomic effects. Overall, the NMPC framework developed here is intended to aid in the evaluation of optimal Non-Pharmaceutical Interventions (NPI) towards spread mitigation of infectious diseases.

Committee: Manish Kumar Ph.D. (Committee Chair); Shelley Ehrlich M.D. (Committee Member); Subramanian Ramakrishnan Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Computer Science and Engineering

3D modeling of non-rigid objects, such as elastic materials and splashing liquids, is a challenging task in computer graphics. While traditional simulation-based methods are accurate and able to produce realistic results, they are not favored by the artists. One of the biggest problems with physics-based simulation is that they are usually too slow to satisfy the demanding computational requirement of an interactive design process. Moreover, elastic materials and splashing liquids exhibit complex dynamics properties so it is hard and non-intuitive for the artists to achieve the desired deformation behavior or splash shape via simulations. With the rapid development of virtual reality and additive manufacturing in recent years, there is a growing demand for fast and convenient 3D modeling tools in interactive design applications. In this dissertation, we focus on developing novel methods specifically for elasticity design and modeling of liquid splashes. First, we propose to formulate elasticity design into a constraint optimization problem. Our key idea is to introduce the inexactness into descent methods, by iteratively solving a forward simulation step and a parameter update step in an inexact manner. Our convergence analysis ensures that each step can be safely solved by a fixed number of iterations and the solver is highly compatible with the GPU acceleration. Second, we present a novel system that synthesizes realistic liquid splashes from simple user sketch input. Our system adopts a conditional generative adversarial network trained with physics-based simulation data to produce raw liquid splash models from input sketches, and then applies model refinement processes to further improve their small-scale details. The system considers not only the trajectory of every user stroke but also its speed, which makes the splash model simulation-ready with its underlying 3D flow. Compared with simulation-based modeling techniques through trials and errors, our system (open full item for complete abstract)

Committee: Huamin Wang (Advisor); Mikhail Belkin (Committee Member); Jian Chen (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2018, Mechanical Engineering

All human movement, whether walking, running, hopping, reaching, or grasping, must be robust with respect to unforeseen perturbations. Some of these perturbations are small and occur almost constantly due to muscle and sensor noise, which are internally generated, or minor fluctuations and imperfections in the external environment. Other perturbations, such as a trip or a push, can be larger and have a greater effect on movement. These perturbations can be applied by another human, large obstacles, or even an attached device such as an exoskeleton or an assistive device. In this thesis, we use mathematical models to examine energy-optimal locomotion, focusing on how humans may walk optimally. Specifically we focus on how they may reject perturbations effectively with the least effort during legged locomotion. In the first part, we propose a new, simplified, three-dimensional model of human locomotion, having a 3D rigid body torso, actuated massless legs, and hip torques. This model provides insight into the features of human bipedal locomotion that cannot be captured by point-mass models, in particular those that relate to the control of upper body orientation. Further, this 3D model shows that walking can be less expensive with a 3D upper body, suggesting why previous point-mass models may have over-estimated the energy cost of walking. Our new model maintains the simplicity of understanding and implementation provided by the point-mass model while having greater applicability to the realistic control of humans and bipedal robots. In the second part, we discuss dynamics and optimality in perturbation rejection using simple mathematical models of human walking and running. We show that energy optimal perturbation recovery predicts features of the control seen previously in human locomotion -- for instance, using appropriate foot placement to redirect the leg force to correct for center of mass state deviations from the nominal. Leg force during stance phase is (open full item for complete abstract)

Committee: Manoj Srinivasan (Advisor) Subjects: Mechanical Engineering

Master of Science (MS), Ohio University, 2018, Civil Engineering (Engineering and Technology)

The Yaqui River Basin (YRB) is located in the semi-arid state of Sonora in northwest Mexico. This watershed flow is controlled by three reservoirs: Angostura, Novillo, and Oviachic. In order to assess and improve the management of the Yaqui reservoir system, a daily reservoir operation model was developed. This model is composed of a semi-distributed daily watershed simulation combined with an optimization model. The hydrologic simulation was developed using the Hydrologic Modeling System (HEC-HMS) developed by the US Army Corps of Engineers (USACE) Hydrologic Engineering Center. This simulation framework can estimate the water availability in different regions of the watershed. The HEC-HMS model was integrated to a nonlinear optimization model that estimates the water allocation in order to satisfy the competing water demands from different users according to water rights established in Mexico's National Water Law. The optimization model is developed using the General Algebraic Modeling System (GAMS). The communication between HEC-HMS and GAMS was completely automated via Python scripts for time efficiency reasons. Different hydrological forcing (precipitation, temperature, and solar radiation) scenarios were applied to the HEC-HMS simulation: (1) a network of daily ground observations from Mexican water agencies during the historical period of 1990-2010, (2) gridded fields from the North America Land Data Assimilation System (NLDAS) at 12 km resolution during the same simulation period, and (3) bias corrected NLDAS fields. The NLDAS forcing and bias corrected NLDAS performed better for Angostura and Novillo whereas ground observation datasets provided the best simulation for Oviachic. The simulated reservoir releases were compared to ideal releases at the three reservoirs to generate confidence in the simulation tools. It was concluded that authorized water allocation is unable to satisfy all users' demands. The simulated water allocation satisfied these demand (open full item for complete abstract)

Committee: Daniel Che (Advisor); Guy Riefler (Committee Member); Agustin Robles-Morua (Committee Member); Derek Kauneckis (Committee Member) Subjects: Agricultural Economics; Agricultural Engineering; Atmosphere; Atmospheric Sciences; Civil Engineering; Computer Science; Engineering; Environmental Engineering; Environmental Science; Hydrologic Sciences; Hydrology; Operations Research; Sustainability; Water Resource Management

Master of Science, The Ohio State University, 2017, Civil Engineering

This thesis aims to use a classical nonlinear programming method for the analysis of cable and tensegrity structures by minimizing the total structural potential energy with the memoryless quasi-Newton method. The motivation for this approach is that cable structures are usually under-constrained structures, and therefore cannot be analyzed using current commercial finite element software. An energy function is derived in terms of nodal coordinates which are the variables of the nonlinear programming problem. The nodal coordinates shift iteratively during the energy minimization process until an optimum configuration is achieved. The proposed method is particularly useful for structures that contain pre-stressed elements such as tensegrity. It provides a unifying approach for analysis of three different types of structures: cable net, cable dome, and tensegrity structural systems. Example analyses of each are presented. The results are compared with experimental results reported in previous papers to demonstrate the accuracy of the proposed approach.

Committee: Hojjat Adeli (Advisor); Abdollah Shafieezadeh (Committee Member); Lisa Burris (Committee Member) Subjects: Civil Engineering; Mathematics

Doctor of Philosophy, The Ohio State University, 2016, Biomedical Engineering

The research presented in this work seeks to develop, validate, and deploy practical techniques for improving diagnosis of cardiovascular disease. In the philosophy of biomedical engineering, we seek to identify an existing medical problem having significant societal and economic effects and address this problem using engineering approaches. Cardiovascular disease is the leading cause of mortality in the United States, accounting for more deaths than any other major cause of death in every year since 1900 with the exception of the year 1918. Cardiovascular disease is estimated to account for almost one-third of all deaths in the United States, with more than 2150 deaths each day, or roughly 1 death every 40 seconds. In the past several decades, a growing array of imaging modalities have proven useful in aiding the diagnosis and evaluation of cardiovascular disease, including computed tomography, single photon emission computed tomography, and echocardiography. In particular, cardiac magnetic resonance imaging is an excellent diagnostic tool that can provide within a single exam a high quality evaluation of cardiac function, blood flow, perfusion, viability, and edema without the use of ionizing radiation. The scope of this work focuses on the application of engineering techniques for improving imaging using cardiac magnetic resonance with the goal of improving the utility of this powerful imaging modality. Dynamic cine imaging, or the capturing of movies of a single slice or volume within the heart or great vessel region, is used in nearly every cardiac magnetic resonance imaging exam, and adequate evaluation of cardiac function and morphology for diagnosis and evaluation of cardiovascular disease depends heavily on both the spatial and temporal resolution as well as the image quality of the reconstruction cine images. This work focuses primarily on image reconstruction techniques utilized in cine imaging; however, the techniques discussed are also relevant t (open full item for complete abstract)

Committee: Orlando P. Simonetti PhD (Advisor); Lee C. Potter PhD (Committee Member); Rizwan Ahmad PhD (Committee Member); Jun Liu PhD (Committee Member) Subjects: Applied Mathematics; Electrical Engineering; Health; Health Care; Medical Imaging; Medicine; Radiology; Scientific Imaging

Master of Science, Miami University, 2015, Mechanical and Manufacturing Engineering

Axially-loaded structures play an integral role in engineering design. Some of these structures exhibit nonlinear response behavior under harmonic loading. Methods aimed at eliminating these behaviors are often employed in design. The question, however, arises: are there any beneficial behaviors that are unknowingly being eliminated? If so, can the nonlinear dynamics be exploited for design benefits? Our hypothesis is that the nonlinear dynamics can be used to optimize system response characteristics. In this thesis, the dynamic behavior of straight and buckled beams under harmonic excitation is considered. Beam models with various sources of nonlinearity are presented and numerical methods are employed to simulate system responses. An optimization approach is formulated that achieves maximized, periodic, and stable responses of the beam systems. Case studies are presented that demonstrate the ability, efficiency and robustness of the optimization approach to exploit the nonlinear dynamics to achieve desired responses.

Committee: Amit Shukla Ph.D. (Advisor); Timothy Cameron Ph.D. (Committee Member); Kumar Singh Ph.D. (Committee Member); William Olson (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2015, Agricultural, Environmental and Developmental Economics

This dissertation consists of three chapters: the implications of environmental policy on nutrient outputs in agricultural watersheds; an assessment of the effects of global wood biomass demand projections on forests using the Global Timber Model (GTM), including heterogeneous products in the forestry sector; and the analysis of whether forest-based bioenergy is carbon neutral using the Monte Carlo analysis with the Global Timber Model (GTM). The first chapter examines whether the federally sponsored voluntary environmental programs to reduce phosphorus pollution from agriculture have had any impact on water quality outcomes. Using daily observations on nutrient emissions taken over 37 years in two Great Lakes tributaries, we estimate an econometric model of phosphorus emissions. Phosphorus emissions are the most important contributor to harmful algal blooms, which have recently caused significant health concerns. Our results indicate that these voluntary programs have had very little effect on phosphorus outputs. In contrast, we show that an input tax could be effective in reducing phosphorus pollution, and consequently, the likelihood of future harmful algal blooms. The second chapter uses the Global Timber Model (GTM) to analyze global biomass demand projection scenarios. The current literature in the Global Timber Model lacks implications of diverse utilizations in forests, assuming a homogeneous product of woody use. In this chapter, the model maximizes the present value of net social welfare derived from harvesting and managing the world's forests and assumes that the timber market consists of two heterogeneous goods: saw-timber and pulpwood. A functioning market for cellulosic biomass does not yet exist; however, we assume that either type of wood is an available feedstock for production of cellulosic bioenergy on the global scale, and that it can be substituted for the purposes of making ethanol or other energy, such as electricity and heat. A baseli (open full item for complete abstract)

Committee: Brent Sohngen (Advisor); Ian Sheldon (Committee Member); Abdoul Sam (Committee Member) Subjects: Environmental Economics

Doctor of Philosophy, The Ohio State University, 2014, Electrical and Computer Engineering

This work presents the development of an integrated flight controller for a generic model of a hypersonic air-breathing vehicle. The flight control architecture comprises a guidance and trajectory planning module and a nonlinear inner-loop adaptive controller. The emphasis of the controller design is on achieving stable tracking of suitable reference trajectories in the presence of a specific engine fault (inlet unstart), in which sudden and drastic changes in the vehicle aerodynamics and engine performance occur. First, the equations of motion of the vehicle for a rigid body model, taking the rotation of the Earth into account, is provided. Aerodynamic forces and moments and engine data are provided in lookup-table format. This comprehensive model is used for simulations and verification of the control strategies. Then, a simplified control-oriented model is developed for the purpose of control design and stability analysis. The design of the guidance and nonlinear adaptive control algorithms is first carried out on a longitudinal version of the vehicle dynamics. The design is verified in a simulation study aiming at testing the robustness of the inner-loop controller under significant model uncertainty and engine failures. At the same time, the guidance system provides reference trajectories to maximize the vehicle's endurance, which is cast as an optimal control problem. The design is then extended to tackle the significantly more challenging case of the 6-degree-of-freedom (6-DOF) vehicle dynamics. For the full 6-DOF case, the adaptive nonlinear flight controller is tested on more challenging maneuvers, where values of the flight path and bank angles exceed the nominal range defined for the vehicle. Simulation studies show stable operation of the closed-loop system in nominal operating conditions, unstart conditions, and during transition from sustained scramjet propulsion to engine failure mode.

Committee: Andrea Serrani Prof. (Advisor); Umit Ozguner Prof. (Committee Member); Zhang Wei Prof. (Committee Member) Subjects: Aerospace Engineering; Computer Engineering; Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2005, Civil Engineering

A multi-paradigm nonparametric model, dynamic fuzzy wavelet neural network (WNN) model, is developed for structural system identification of three dimensional highrise buildings. The model integrates chaos theory (nonlinear dynamics theory), a signal processing method (wavelets), and two complementary soft computing methods (fuzzy logic and neural network). An adaptive Levenberg-Marquardt-least-squares learning algorithm is developed for adjusting parameters of the dynamic fuzzy WNN model. The methodology is applied to one five-story test frame and two highrise moment-resisting building structures. Results demonstrate that the methodology incorporates the imprecision existing in the sensor data effectively and balances the global and local influences of the training data. It therefore provides more accurate system identifications and nonlinear approximation with a fast training convergence. A nonparametric system identification-based model is developed for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy WNN model. The model does not require complete measurements of the dynamic responses of the whole structure. A damage evaluation method is proposed based on a power density spectrum method. The multiple signal classification method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using experimental data obtained for a 38-story concrete test model. It is demonstrated that the WNN model together with the pseudospectrum method is effective for damage detection of highrise buildings based on a small amount of sensed data. A nonlinear control model is developed for active control of highrise three dimensional building structures including geometrical and material nonlinearities, coupling action between lateral and torsional motions, and actuator dynamics. A dynamic fuzzy wavelet neuroemulator is developed for predicting the structural response in futur (open full item for complete abstract)

Committee: Hojjat Adeli (Advisor) Subjects: Engineering, Civil

Master of Science (MS), Ohio University, 1991, Electrical Engineering & Computer Science (Engineering and Technology)

A physical-based nonlinear model for the GaAs MESFET with parameter optimization

Committee: M. Mokari (Advisor) Subjects:

Doctor of Philosophy (Ph.D.), University of Dayton, 2012, Electrical Engineering

Human intelligence has appealed to the robotics community for a long time; specifically, a person's ability to learn new tasks efficiently and eventually master the task. This ability is the result of decades of development as a person matures from an infant to an adult and a similar developmental period seems to be required if robots are to obtain the ability to learn and master new skills. Applying developmental stages to robotics is a field of study that has been growing in acceptance. The paradigm shift is from directly pursuing the desired task to progressively building competencies until the desired task is reached. This dissertation seeks to apply a developmental approach to autonomous optimization of robotic motions, and the methods presented extend to function shaping and parameter optimization. Humans have a limited ability to concentrate on multiple tasks at once. For robots with many degrees of freedom, human operators need a high-level interface, rather than controlling the positions of each angle. Motion primitives are scalable control signals that have repeatable, high-level results. Examples include walking, jumping or throwing where the result can be scaled in terms of speed, height or distance. Traditionally, motion primitives require extensive, robot-specific analysis making development of large databases of primitives infeasible. This dissertation presents methods of autonomously creating and refining optimal inverse functions for use as motion primitives. By clustering contiguous local optima, a continuous inverse function can be created by interpolating results. The additional clusters serve as alternatives if the chosen cluster is poorly suited to the situation. For multimodal problems, a population based optimization can efficiently search a large space. Staged learning offers a path to mimic the progression from novice to master, as seen in human learning. The dimension of the input wave parameterization, which is the number degrees of freed (open full item for complete abstract)

Committee: Raúl Ordóñez Ph. D. (Advisor); Frederick G. Harmon Ph. D., Lt Col (Committee Member); Eric Balster Ph. D. (Committee Member); Andrew Murray Ph. D. (Committee Member) Subjects: Applied Mathematics; Artificial Intelligence; Electrical Engineering; Robotics; Robots

Master of Science in Electrical Engineering, Cleveland State University, 2011, Fenn College of Engineering

We discuss open loop control development and simulation results for a semi-active above-knee prosthesis. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. We develop open loop control using biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm, and gradient descent. We use gradient descent to show that the control generated by BBO is locally optimal. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to complex, real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective open loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The control algorithm provides knee angle tracking with an RMS error of 7.9 degrees, and thigh angle tracking with an RMS error of 4.7 degrees. Robustness tests show that the BBO control solution is affected very little by disturbances added during the simulation. However, the open loop control is very sensitive to the initial conditions. So a closed loop control is needed to mitigate the effects of varying initial conditions. We implement a proportional, integral, derivative (PID) controller for the prosthesis and show that it is not a sufficient form of closed loop control. Instead, we implement artificial neural networks (ANNs) as the mechanism for closed loop control. We show that ANNs can greatly improve performance when noise and disturbance cause high tracking errors, thus reducing the risk of stumbles and falls. We also show that ANNs are able to improve average performance by as much as 8% over open loop control. We also discuss embedded system implementation with a microcontroller and associated hardware and software.

Committee: Dan Simon PhD (Advisor); Fuquin Xiong PhD (Committee Member); Lili Dong PhD (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

The ubiquitous influence of fossil fuels in driving the world economy and the imperative need to reduce dependence of transportation on these fuels, has brought about a decade of research on alternative propulsion systems. Of the several alternative propulsion systems, hybrid electric vehicles (HEVs) are seen as an important short-term solution. In the most generic sense, a HEV consists of a battery and one or more electric machines in addition to the engine powered by petroleum/diesel. Depending on the vehicle architecture, the additional degree of freedom in selecting the amount of energy supplied by the primary and the secondary source of energy is a challenging control and optimization problem. The energy management strategy in a HEV aims at finding the optimal distribution of energy between the battery and the fuel to satisfy the requested power from the driver.Different energy management strategies have been developed both by the industry and the academia and they can be classified into non-realizable and realizable energy management strategies based on the amount of information required for real-time implementation. Traditionally, the non-realizable strategies formulate the energy management problem as a constrained optimal control problem of minimizing a performance index over a finite time interval under operational constraints. These strategies provide the global optimal solution and are used as benchmark solutions for comparative analysis of strategies. The realizable strategies in the literature have been primarily developed for implementation in real vehicles and have been shown to produce results similar to the global optimal solution. In spite of the extensive amount of research on both non-realizable and realizable energy management strategies, there are many shortcomings in the literature which have been addressed in this dissertation. The energy management problem of finding the optimal split between the different sources of energy in a charge-sust (open full item for complete abstract)

Committee: Giorgio Rizzoni Professor (Advisor); Stephen Yurkovich Professor (Committee Member); Vadim Utkin Professor (Committee Member); Yann Guezennec Professor (Committee Member); Simona Onori PhD (Committee Member) Subjects: Alternative Energy; Automotive Engineering; Electrical Engineering; Mechanical Engineering