Doctor of Philosophy in Engineering, Cleveland State University, 2019, Washkewicz College of Engineering

We investigate the control and optimization of robots with ultracapacitor based regenerative drive systems. A subset of the robot joints are conventional, in the sense that external power is used for actuation. Other joints are energetically self-contained passive systems that use ultracapacitors for energy storage. An electrical interconnection known as the star configuration is considered for the regenerative drives that allows for direct electric energy redistribution among joints, and enables higher energy utilization efficiencies. A semi-active virtual control strategy is used to achieve control objectives. We find closed-form expressions for the optimal robot and actuator parameters (link lengths, gear ratios, etc.) that maximize energy regeneration between any two times, given motion trajectories. In addition, we solve several trajectory optimization problems for maximizing energy regeneration that admit closed-form solutions, given system parameters. Optimal solutions are shown to be global and unique. In addition, closed-form expressions are provided for the maximum attainable energy. This theoretical maximum places limits on the amount of energy that can be recovered. Numerical examples are provided in each case to demonstrate the results. For problems that don't admit analytical solutions, we formulate the general nonlinear optimal control problem, and solve it numerically, based on the direct collocation method. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA manipulator with custom regenerative drives. Power flows, stored regenerative energy and efficiency are evaluated. Experimental results show that when following optimal trajectories, a reduction of about 10-22% in energy consumption can be achieved. Furthermore, we present the design, control, and experimental evaluation of an energy regenerative powered transfemoral prosthesis. Our prosthesis prototype is comprised of a passive (open full item for complete abstract)

Committee: Hanz Richter (Advisor); Dan Simon (Committee Member); Eric Schearer (Committee Member); Antonie van den Bogert (Committee Member); Ulrich Zurcher (Committee Member) Subjects: Engineering; Mechanical Engineering; Robotics

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

Robotic lower limb prostheses can improve the quality of life for amputees. Development of such devices, currently dominated by long prototyping periods, could be sped up by predictive simulations. In contrast to some amputee simulations, which track experimentally determined non-amputee walking kinematics, we can instead explicitly model the human-prosthesis interaction to produce a prediction of the user's walking kinematics. To accomplish this, we use large-scale trajectory optimization on a muscle-driven multi-body model of an amputee with a robotic prosthesis to obtain metabolic energy-minimizing walking gaits. While this computational framework can be applied to a wide range of passive or biomechatronic prosthetic, exoskeletal, and assistive devices, here, we focus on unilateral ankle-foot prostheses. We use this optimization to determine optimized prosthesis controllers by minimizing a weighted sum of human metabolic and prosthesis costs and develop Pareto optimal curves between human metabolic and prosthesis cost with various prostheses masses and at various speeds. We also use this optimization to obtain trends in the energetics and kinematics for various net prosthesis work rates produced by given prosthesis feedback controllers. We find that the net metabolic rate has a roughly quadratic relationship with the net prosthesis work rate. This simulation predicts that metabolic rate could be reduced below that of a non-amputee, although such gaits are highly asymmetric and not seen in experiments with amputees. Walking simulations with bilateral symmetry in kinematics or ground reaction forces have higher metabolic rates than asymmetric gaits, suggesting a potential reason for asymmetries in amputee walking. Our findings suggest that a computational framework such as one presented here could augment the experimental approaches to prosthesis design iterations, although quantitatively accurate predictions of experiments from simulation remains an open probl (open full item for complete abstract)

Committee: Manoj Srinivasan (Advisor); Steve Collins (Committee Member); Kiran D'Souza (Committee Member); Rob Siston (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, Miami University, 2024, Electrical and Computer Engineering

Optimizing user throughput is crucial for practical deployment in the developing field of Unmanned Aerial Vehicle (UAV) -assisted vehicular communications. This thesis introduces UAV-assisted vehicle-to-infrastructure (V2I) communication using UAVs as base stations (BS) to maximize system throughput while minimizing energy consumption. We investigate UAV positioning on a highway, considering vehicle mobility. Our approach combines discrete user association and subchannel allocation with continuous UAV trajectory and power control, addressing challenges in mixed-integer non-linear programming. The optimization ensures UAV trajectories adhere to realistic flight dynamics, incorporating acceleration and enforcing collision avoidance and operational bounds. Additionally, it takes into account the energy dynamics of UAVs, including both flying power and communication power. The complex optimization problem is resolved using a genetic algorithm to achieve a solution of UAV trajectory, subchannel allocation, power allocation, and vehicle association. A baseline state is configured considering initial UAV position, speed, subchannel allocation, initial association, and vehicle speed. A trade-off in the weighting factor of the optimization problem objective function shows a 37-65% increase in total data rate and a reduction of 11-19% in energy consumption.

Committee: Mark J Scott (Advisor); Bryan Van Scoy (Committee Member); Miao Wang (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

There is substantial research interest in developing prostheses for amputated lower limbs that allow amputees to move dynamically with the same ease as non-amputee persons. Recent developments in this field involve robotic powered ankle prostheses. In contrast to conventional and energy storing prostheses, robotic powered ankle prostheses include actuators and controllers and can produce positive net work. Despite proposing a number of promising candidates for the design of such devices, researchers have largely struggles to realize their theoretical potential. Despite the years of engineering and iteration invested in each prototype design, at the end of the design process, human subject testing routinely reveals that amputees do not benefit from the device as predicted. The effort required for an amputee to walk with a powered ankle prostheses usually fails to improve relative to non-amputee walking or amputee walking with simpler energy storage devices. Additionally, the physical and psychological comfort of novel designs often fail to show progress from the status quo. In this work, we attempt to contribute improvements to the design process for robotic powered prosthetic ankle devices by developing novel computational design methods and tools principally aimed at the design of compliant linkages. A new kinetostatic synthesis problem for a general compliant four-bar linkage is proposed as a min-max problem. The method is data-driven and can be used to design personalized devices. It can also fuse data sources across walking speeds, subjects, or other motions, to design a single device to accommodate a variety of motions. A new gait predictive simulation problem is proposed. For the first time, computational gait prediction is demonstrated for the human-robot hybrid system that includes detailed dynamic models of robotic prostheses. These models include closed-loop linkages for transmission, compliant elements, and internal actuator dynamics. The rel (open full item for complete abstract)

Committee: Haijun Su (Advisor); Herman Shen (Committee Member); Manoj Srinivasan (Committee Member) Subjects: Biomechanics; Engineering; Mechanical Engineering

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

Collegiate athletes rely on their muscles to compete in their respective sports. However, one injury requiring extended time out of competition could lead to muscle atrophy. As a result, athletes may learn to compensate for weakened muscle groups with stronger muscle groups; a change that may be almost undetectable. Consequently, compensating can add unnecessary stress to the musculoskeletal system, leading to reinjury. One way to combat this is by measuring muscle force. However, there are currently no methods to directly measure muscle force, so it must be solved for indirectly. This research aims to explore state estimation with trajectory optimization and a convolutional neural network. Both methods will be used to estimate the trajectories of the state variables and muscle force associated with forearm flexion. To serve as an input to both solution methods, artificial data was generated. This data contained measured trajectories for forearm position, angular velocity, muscle fiber length, muscle activation, and muscle force. In addition, the generated data included artificial sensor signals comprised of an electromyography (EMG) and inertial measurement unit (IMU). For testing, different signal to noise ratios were added to the generated sensor data. The trajectory optimization method was tested using different weight ratios. The results from this simulation study confirm that the tuning parameter should be chosen based on the noise levels present within the data. Moreover, this method of state estimation can accurately and precisely predict state variable trajectories at all noise levels. However, it struggles to predict muscle force when there is noise added to the data. A similar process was conducted to test the neural network; however, the batch size, was the tuning parameter selected for this method. The results from this portion of the simulation study conclude that the convolutional neural network was able to estimate the state variables precise (open full item for complete abstract)

Committee: Antonie van den Bogert (Advisor); Eric Schearer (Committee Member); Majid Rashidi (Committee Member) Subjects: Biomechanics; Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2020, Electrical Engineering & Computer Science (Engineering and Technology)

In this dissertation, the problem of trajectory design for autonomous wheeled ground vehicles are investigated. Several line-of-sight (LOS) based trajectory design approaches are developed to solve the problem in various practical scenarios. For path planning of on-road driving, a LOS pure pursuit guidance (PPG) path planner is designed. Stability analysis for LOS PPG along a general reference path is conducted based on Lyapunov stability theory. By using the theoretical analysis results, a design guideline for the selection of the guidance parameters is derived. The geometric interpretation of LOS PPG for general guidance parameters is provided. Then, for a given feasible, collision-free path, the problem of converting the geometric path to a time-parameterized trajectory is studied. A novel receding-horizon type sub-optimal path-to-trajectory conversion algorithm is developed which is able to take into account dynamic constraints and has high computational efficiency. For the problem of path planning for autonomous car-like ground vehicle parking, a novel four-phase path planning algorithm is developed. The algorithm is able to cope with various parking scenarios in a unified, scalable manner with low computational cost. The four-phase algorithm is extended to standard N-trailer parking and a novel cascade path planning algorithm is developed. Besides the advantages inherited from the four-phase algorithm, the cascade algorithm for standard N-trailer parking is able to prevent jackknife phenomenon.

Committee: Jim Zhu (Advisor); Douglas Lawrence (Committee Member); Robert Williams II (Committee Member); Frank Van Graas (Committee Member); Xiaoping Shen (Committee Member); Sergio Ulloa (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Engineering

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Aerospace Engineering

A novel approach to parameterize and solve for optimal satellite attitude state trajectories is presented. The optimal trajectories are parameterized using fuzzy inference systems (FISs), and the FISs are optimized using a genetic algorithm. Eight different constrained optimization problems are solved. The objective of each optimization problem is either battery charge maximization, link margin (equivalent to antenna gain) maximization, or experiment temperature minimization. All optimization problems consider reaction wheel angular velocity and reaction wheel angular acceleration constraints, and five of the optimization problems consider either battery charge constraints, antenna gain constraints, or both battery charge and antenna gain constraints. Reaction wheel constraints are satisfied using an attitude state filter at the output of the FISs and an optimal magnetic torque / reaction wheel desaturation algorithm, the design of both of which is presented herein. Optimal attitude state trajectory, or attitude profile, FISs are compared with a nominal attitude profile. It is shown that, while the nominal attitude profile offers good performance with respect to both battery charge and link margin, the optimal attitude profile FISs are able to outperform the nominal profile with respect to all objectives, and a minimum temperature attitude profile FIS is able to achieve average experiment temperatures 30–40 K lower than the nominal attitude profile. The attitude state trajectory optimization solutions presented in this work are motivated by the needs and constraints of the CryoCube-1 mission. Because this work is integral to the functionality of the CryoCube-1 satellite system, the effort taken to successfully build, test, deliver, launch, and deploy this CubeSat is detailed. The intent of providing this systems view is to provide the context necessary to understand exactly how the attitude state trajectory optimization results were used within the satellite system.

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member); Phil Putman Ph.D. (Committee Member); Anoop Sathyan Ph.D. (Committee Member) Subjects: Aerospace Materials

Doctor of Philosophy in Engineering, Cleveland State University, 2020, Washkewicz College of Engineering

The method of trajectory optimization with direct collocation has the potential to extract generalized and realistic motion controllers from long duration movement data without requiring extensive measurement equipment. Knowing motion controllers not only can improve clinic assessments on locomotor disabilities, but also can inspire the control of powered exoskeletons and prostheses for better performance. Three aims were included in this dissertation. Aim 1 was to apply and validate the trajectory optimization for identification of the postural controllers in standing balance. The trajectory optimization approach was first validated on the simulated standing balance data and demonstrated that it can extract the correct postural control parameters. Then, six types of postural feedback controllers, from simple linear to complex nonlinear, were identified on six young adults' motion data that was collected in a standing balance experiment. Results indicated that nonlinear controllers with multiple time delay paths can best explain their balance motions. A stochastic trajectory optimization approach was proposed that can help finding practically stable controllers in the identification process. Aim 2 focused on the foot placement control in walking. Foot placement controllers were successfully identified through the trajectory optimization method on nine young adults' perturbed walking motions. It was shown that a linear controller with pelvis position and velocity feedback, suggested by the linear inverted pendulum model, was not sufficient to explain their foot placement among multiple walking speeds. Nonlinear controllers or more feedback signals, such as pelvis acceleration, are needed. Foot placement control was applied on a powered leg exoskeleton to control its legs' swing motion. Two healthy participants were able to achieve stable walking with the controlled exoskeleton. Results suggested that the foot placement controller helped decelerate the swing mo (open full item for complete abstract)

Committee: Antonie van den Bogert Dr. (Advisor); Anne Su Dr. (Committee Member); Hanz Richter Dr. (Committee Member); Dan Simon Dr. (Committee Member); Eric Schearer Dr. (Committee Member) Subjects: Biomechanics; Mechanical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2018, Electrical Engineering

The purpose of this thesis is to examine the ability of evolutionary algorithms (EAs) to develop near optimal solutions to three different path planning control problems. First, we begin by examining the evolution of an open-loop controller for the turn-circle intercept problem. We then extend the evolutionary methodology to develop a solution to the closedloop Dubins Vehicle problem. Finally, we attempt to evolve a closed-loop solution to the turn constrained pursuit evasion problem. For each of the presented problems, a custom controller representation is used. The goal of using custom controller representations (as opposed to more standard techniques such as neural networks) is to show that simple representations can be very effective if problem specific knowledge is used. All of the custom controller representations described in this thesis can be easily implemented in any modern programming language without any extra toolboxes or libraries. A standard EA is used to evolve populations of these custom controllers in an attempt to generate near optimal solutions. The evolutionary framework as well as the process of mixing and mutation is described in detail for each of the custom controller representations. In the problems where an analytically optimal solution exists, the resulting evolved controllers are compared to the known optimal solutions so that we can quantify the EA's performance. A breakdown of the evolution as well as plots of the resulting evolved trajectories are shown for each of the analyzed problems.

Committee: Zachariah Fuchs Ph.D. (Advisor); John Gallagher Ph.D. (Committee Member); Luther Palmer Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Engineering, Cleveland State University, 2018, Washkewicz College of Engineering

Predictive simulations predict human gait by solving a trajectory optimization problem by minimizing energy expenditure. These simulations could predict the effect of a prosthesis on gait before its use. This dissertation has four aims, to show the application of predictive simulations in prosthesis design and to improve the quality of predictive simulations. Aim 1 was to explain joint moment asymmetry in the knee and hip in gait of persons with a transtibial amputation (TTA gait). Predictive simulations showed that an asymmetric gait required less effort. However, a small effort increase yielded a gait with increased joint moment symmetry and reduced joint reaction forces. This suggests that gait training could reduce the risk of developing osteoarthritis in persons with a transtibial amputation. Aim 2 was to compare the effect of different prosthesis alignments on TTA gait. Predictive simulations were solved using a three-dimensional musculoskeletal model with different prosthetic alignments. A flexion alignment of the prosthesis might be favored over a neutral alignment, since the metabolic cost and joint reaction forces were lower, though the differences were small. Also, predictions indicated that a lateral translation or an external rotation could alleviate skin problems by reducing skin-to-socket stresses. Aim 3 was to compare the gait objective of minimizing metabolic energy to minimizing muscular effort. Four metabolic energy expenditure models were selected after an experiment to compare metabolic cost calculated with seven metabolic energy models to metabolic cost from pulmonary gas exchange measurements. The minimum energy solution was more similar to normal gait in joint angles, while the minimum effort solution was more similar in joint moments, especially at the knee. However, neither solution could entirely explain human gait. Aim 4 was to propose an approach to optimize in a stochastic environment and implement it to explain antagonistic muscle co-c (open full item for complete abstract)

Committee: Antonie van den Bogert (Advisor); Ann Rheinthal (Committee Member); Hanz Richter (Committee Member); Eric Schearer (Committee Member); Daniel Simon (Committee Member) Subjects: Biomechanics; Mechanical Engineering; Rehabilitation

MS, University of Cincinnati, 2016, Engineering and Applied Science: Mechanical Engineering

This thesis focuses on developing optimization algorithms for path planning of single and cooperating Unmanned Air Vehicles (UAVs), operating in National Air Space (NAS), in presence of other moving and/or stationary obstacles. The problem is formulated in the framework of Mixed Integer Linear Programming (MILP) which has been proven to be efficient in literature for solving optimization problems in other domains well as path planning problems. This thesis extends the works carried out in literature via proposing the cost-to-go function that incorporates a number of criteria such as path length, uncertain nature of NAS environment, and time and energy consumption based on detailed dynamical model of motion in three dimensions taking into consideration various UAV constraints. The problem is first formulated using single vehicle and then extended to multiple vehicles having a common goal which is incorporated using motion constraints. The solution of the MILP is based on a fast Floating Point (FP) method and is provided in detail in this thesis. This method results in decrease of the computational effort. Incorporation of the moving obstacles or Intruder Aircrafts (IAs) in the problem is done using Kalman filter and Bayesian framework that enable us to simulate uncertainty in motion of obstacles (or intruder aircraft) and maintain the distance between the UAV fleet and other non-cooperative airplanes in NAS. In result, this approach enables simulation of vehicles in team while guaranteeing the robust fleet in uncertain domain. Bayesian method helps us to overcome the hindrance of implementing this algorithm in dynamic and uncertain environment including IAs and pop-up threats. The proposed methodology for solving cooperative form of centralized control in the framework of MILP for cooperative UAVs is shown to result in robust solutions and improves overall team performance. All the algorithms are tested and demonstrated via a number of numerical studi (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); Kelly Cohen Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Engineering

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

MS, University of Cincinnati, 2005, Engineering : Aerospace Engineering

Due to the runway threshold and airport capacity constraints, aircraft are often required to delay their arrival time when they are approaching the TRACON (Terminal Radar Approach Control) area to meet separation requirements and to ensure safety. This is particularly true in the US in the northeast corridor, where sectors are small, with shorter controllable time, and involving very complex and heavy traffic flows. In this situation, downstream schedule constraints may be passed upstream, most likely across multiple ARTCCs (Air Route Traffic Control Centers) and multiple sectors. More sectors may be needed to absorb the required delay. The technical issue for delaying aircraft over extended region is that uncertainties in flight time, and the rather close tolerance on final spacing, make delay predictions far into the future rather suspect. This paper provides a delay strategy that the problem of distributing delay across multiple sectors is addressed as a discrete optimal control problem. Game theory, coupled with dynamic programming (DP) is used in this research to give an optimal solution for the delay controls in each sector. In this application the sector delay is chosen to minimize a performance index and the uncertainty is viewed as an adversary trying to maximize the performance index. This DP approach is capable of creating a favorable delay distribution solution and the solution is fuel efficient. It is easy for controller to implement because the algorithm is computationally efficient, the method can quickly reallocate the delay by adjusting the model parameters to provide a robust solution. As currently formulated the DP algorithm ensures only separation at the terminal fix. However, at several intermediate points, the traffic may merge into a single stream from several directions. An algorithm is developed to integrate the DP algorithm so as to solve the intermediate merging conflict as well as ensuring terminal separation. The validity of this mechani (open full item for complete abstract)

Committee: Dr. Gary Slater (Advisor) Subjects: