Master of Science (MS), Ohio University, 2016, Electrical Engineering (Engineering and Technology)

A vehicle-to-vehicle follower design based on RC Unmanned Ground Vehicle (UGV) is presented in this thesis. To achieve the desired performance for two-vehicle leader-follower, a 3DOF path trajectory tracking controller with a close-loop guidance controller are used, which consider both the kinematics and the dynamics characteristics of the vehicle model. In our research, we use a Trajectory Linearization Control (TLC) to achieve the path tracking, and a PID controller to guide the preceding vehicle. To this end, the following objectives have been achieved. First, a 3DOF kinematics and dynamics vehicle model has been built. Second, an Adaptive Cruise Control (ACC) Trajectory Linearizaton Control (ACCTLC) scheme is presented. Third, a Vehicle-to-Vehicle following Trajectory Linearizaiton Control is proposed. MATLAB/SIMULINK simulation testing of 3DOF control algorithm is presented, which verifies the algorithm. Future work include implementing the current controller design by installing those algorithms to the real RC car; as well as adding lane constraint to the current work; and adding obstacle avoidance to develop fully autonomous ground vehicle.

Committee: Michael Braasch (Advisor); Jim Zhu (Committee Co-Chair) Subjects: Electrical Engineering

Master of Science (MS), Ohio University, 2006, Electrical Engineering & Computer Science (Engineering and Technology)

This thesis presents derivation of a trajectory tracking and stair climbing stabilization controller for a 4x4 skid-steered wheeled mobile robot (SSWR). The robot vehicle is a sturdy platform actuated by DC motors capable of traversing difficult terrain. For trajectory tracking, an essential capability for autonomous operation, a reliable and robust controller is needed. In addition, as the vehicle is unstable with manual control while climbing stairs, the controller is required to stabilise it during stair/ramp climb. The robot vehicle is modelled with six degrees of freedom (6DOF) rigid body equations and an efficient control algorithm, called Trajectory Linearisation Control (TLC), is used to tackle the challenges posed by nonlinearities of the model. In TLC, state dynamics are linearised along the trajectory being tracked and PI control is used to stabilise tracking error dynamics. Kinematics and dynamics are controlled individually using feedback loops, where the former constitutes the outermost loop. The main contribution of this work is analysis of 6DOF physical model and a consolidated simple controller for planar tracking and stair climbing stabilization for an SSWR. Simulation results promise that a stable climb on 20° steep staircase is possible with current vehicle configuration. Monte Carlo simulations prove that the controller is robust to realistic dispersions of frictional and physical parameters. Effects of perturbations in these parameters have been studied and improvements in mechanical design are suggested.

Committee: Jianchao Zhu (Advisor) Subjects:

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

As passenger vehicle technologies have advanced, so have their capabilities to avoid obstacles, especially with developments in tires, suspensions, steering, as well as safety technologies like ABS, ESC, and more recently, ADAS systems; however, environments around passenger vehicles have also become more complex, and dangerous. As autonomous road vehicle (ARV) development aims to address these complex environments, one area that is still new and open is ARV emergency obstacle avoidance at highway speeds (55-165 km/h) and on slippery road surfaces. When introducing obstacle avoidance capabilities into an ARV, it is important to target performance that meets or exceeds that of human drivers. This dissertation highlights subsystems within an entire ARV, which are crucial for the completion of a highly functional emergency obstacle avoidance maneuver (EOAM), and combines them in a novel framework while considering the nuances of traveling at highway speeds and/or slippery road surfaces. The primary subsystems developed and tested in this research include the synthesis of ARV sensing, perception, decision making, control, and actuation. These subsystems are introduced with some novelties to the current state-of-the-art as well as the holistic ARV EOAM Framework, designed to handle highway speeds and slippery surfaces, as a novelty. Lastly, a newly considered testing and validation methodology for ARV EOAM performance and validation is presented. This general obstacle avoidance capability assessment (GOACA) has implications for adoption by national or even global regulation bodies, regarding ARV EOAM safety performance while requiring all the core ARV systems to perform well, and in harmony, to achieve top marks

Committee: Levent Güvenç (Advisor); Ayonga Hereid (Committee Member); Mrinal Kumar (Committee Member); Bilin Aksun-Güvenç (Committee Member) Subjects: Automotive Engineering; Computer Science; Engineering; Mechanical Engineering; Physics; Robotics; Transportation

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

Connected and automated (CAV) technologies offer new opportunities that can transmit real-time vehicular information, and their trajectories can be precisely controlled, which eliminates the barriers of conventional control framework. However, before the CAVs are prevalent in the traffic stream, a mixed-autonomy environment will last a long period with gradually increasing CAV market penetration. This indicates that traffic management under mixed-autonomy environment is essential in the transition from conventional traffic management to full-autonomy traffic control. To signalized intersection, vehicular trajectory control and signal optimization based on CAV technologies are two approaches that have significant potential to mitigate congestion, lessen the risk of crashes, reduce fuel consumption, and decrease emissions at intersections. Therefore, these two approaches should be integrated into a unified traffic management framework such that both aspects can be optimized simultaneously to achieve maximum benefits. This dissertation proposes a mixed-autonomy traffic management framework to integrate the signal control and trajectory control systematically. The framework architecture consists of six layers, including sensing, information, planning, optimization, control, and evaluation, and each layer has its own scope and responsibility. The proposed framework is a flexible and compatible framework for joint optimization of vehicle trajectory and signal control, and it can be applied for both full-autonomy and mixed-autonomy environments. The development details of major components are also described. A dynamic programming (DP) framework with trajectory planning with piecewise polynomials (TP3) as a subroutine (DP-TP3) is presented to solve the joint optimization of signal control and vehicle trajectory control considering conflicts of the four movements. The proposed TP3 algorithm provides an analytically solvable operation for vehicular trajectory const (open full item for complete abstract)

Committee: Jiaqi Ma Ph.D. (Committee Chair); Na Chen Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member); Nabil Nassif Ph.D. (Committee Member) Subjects: Civil 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

Doctor of Philosophy (PhD), Ohio University, 2016, Electrical Engineering & Computer Science (Engineering and Technology)

In this dissertation, an integrated automatic flight controller for fixed-wing aircraft Loss-of-Control (LOC) Prevention and Recovery (iLOCPR) is designed. The iLOCPR system comprises: (i) a baseline flight controller for six degrees-of-freedom (6DOF) trajectory tracking for nominal flight designed by trajectory linearization, (ii) a bandwidth adaption augmentation to the baseline controller for LOC prevention using the timevarying PD-eigenvalues to trade tracking performance for increased stability margin and robustness in the presence of LOC-prone flight conditions, (iii) a controller reconfiguration for LOC arrest by switching from the trajectory tracking task to the aerodynamic angle tracking in order to recover and maintain healthy flight conditions at the cost of temporarily abandoning the mission trajectory, (iv) a guidance trajectory designer for mission restoration after the successful arrest of a LOC upset, and (v) a supervisory discrete-eventdriven Automatic Flight Management System (AFMS) to autonomously coordinate the control modes (i) - (iv). Theoretical analysis and simulation results are shown for the effectiveness of the proposed methods.

Committee: Jim Zhu (Advisor); Douglas Lawrence (Committee Member); Frank Van Grass (Committee Member); Robert Williams (Committee Member); Aili Guo (Committee Member); Sergiu Aizicovici (Committee Member) Subjects: Aerospace Engineering; 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

Doctor of Philosophy, The Ohio State University, 2011, Mechanical Engineering

This work studies spatial reaching motion in healthy humans. Research suggests that for individual instances of movement, the central nervous system (CNS) composes an explicit wrist path, which is transformed into joint motions in a time-invariant fashion. This is the time invariance hypothesis (TIH), and its validation for spatial motion is the first goal of this study. The human arm is typically modeled as a multi-link, serial chain. When one joint of a serial chain is actuated, it simultaneously causes movement at other joints because of interaction effects. Based on horizontal-plane reaching studies, the leading joint hypothesis (LJH) proposes that the interaction effects at (mostly) the proximal joint in the multi-link serial-chain model of the arm are low. Therefore, the CNS ignores this interaction effect to simplify the computation of joint torques and control of the joint trajectory. The second objective of this dissertation is to validate the LJH for spatial motion. In a spatial reaching experiment, healthy subjects performed point-to-point reaching movements at three distinct speeds. Data analysis revealed time-invariant wrist paths only for some subjects in some reaching tasks, suggesting that the TIH is not a truly general organizing principle for spatial reaching motion. Therefore, this hypothesis needs refinement and further investigation. On the other hand, the interaction effects at the shoulder joint were small for a majority of the movements in this experiment so, the LJH was successfully extended to spatial motion. The TIH identifies the inputs and outputs of the first stage in the process of composing the muscle activations for a given motor task. A computational algorithm that can potentially be used to execute this transformation was developed next. The algorithm, called speed-ratio control, also has beneficial applications in commercial robot control. It is demonstrated that the application of this algorithm to robotic serial chains provides (open full item for complete abstract)

Committee: James P. Schmiedeler Dr (Advisor); Gary L. Kinzel Dr (Advisor); Robert A. Siston Dr (Committee Member); Richard J. Jagacinski Dr (Committee Member) Subjects: Kinesiology; Mechanical Engineering; Robotics

Master of Science (MS), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)

This thesis presents an improvement in the performance of a three degrees of freedom differential thrust control testbed by considering the actuator dynamics. The testbed consists of three propellers that are used to produce thrust as well as attitude control for vertical takeoff and landing flight. Actuator dynamics consist of the motor-propeller dynamics and the nonlinear mapping relating the aerodynamic torques to the propeller speed. A previous controller was designed by neglecting the motor-propeller dynamics and the control allocation was done assuming a linear static relationship between aerodynamic torques and motor voltages. This work will determine the nonlinear control allocation mapping and model the motor-propeller dynamics as a first-order linear system. Simulation and real-time results showing an improvement in the performance of the testbed are presented by replacing the linear control allocation with nonlinear control allocation and by compensating for the motor-propeller dynamics. Further, the existing controller is redesigned considering the gyroscopic effects produced due to the spinning propellers.

Committee: Jianchao Zhu (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)

Advanced nonlinear control design methods usually depend on an analytical plant model, which in many practical applications, is often inaccurate or unavailable. Neural networks are a powerful tool to enhance a dynamic model if one is available but inaccurate, or to build a system dynamic model from experimental data. Trajectory linearization control (TLC) is a nonlinear control design method, which combines nonlinear dynamic inversion and linear time-varying (LTV) feedback stabilization to achieve robust tracking control for a broad class of nonlinear dynamic systems. In this dissertation, the research goal is to develop theories and methodologies to improve the understanding and applicability of TLC for inaccurate or lack of dynamic models by using neural networks. To this end, the following research objectives have been achieved. First, rigorous stability robustness analyses of TLC subject to the regular perturbation and singular perturbation are established. Second, a continuous-time nonlinear system identification method using neural network is developed. Third, a neural network trajectory linearization control (NNTLC) design procedure with stability analysis is proposed. Fourth, an adaptive neural network trajectory linearization control (ANNTLC) scheme is presented, in which the neural network control compensates for the system uncertainty adaptively. Illustrative examples of nonlinear applications of TLC, NNTLC and ANNTLC are also presented.

Committee: J. Zhu (Advisor) Subjects:

Master of Sciences, Case Western Reserve University, 2024, EMC - Mechanical Engineering

Locomoting in the surf-zone is a difficult challenge for robots as they must be able to resist the hydrodynamic forces of waves while overcoming the challenges of walking in sand. However, crabs can successfully navigate this environment. Inward gripping with crab-inspired curved dactyls has been demonstrated to increase the effective weight of a crab-like robot and has been used to reduce the cost of transport, allowing for more feasible operation in the surf-zone. In this thesis, four new gaits were created and tested along with two previously developed gaits on an 18 degreeof-freedom crab-inspired robot. Tests were conducted in both still water and wave conditions with sand. Results show that the gait with a smooth swing path and front only gripping is on average 50% more energy efficient than the gait with a polygonal swing path and front and rear gripping in still water and 29% more efficient in waves.

Committee: Kathryn Daltorio (Committee Chair); Roger Quinn (Committee Member); Richard Bachmann (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2023, Electrical Engineering & Computer Science (Engineering and Technology)

In this dissertation, an integrated nonlinear motion control algorithm is presented for trajectory tracking and rollover prevention and mitigation for wheeled ground vehicles, thereby providing support for level 3 and higher levels of autonomous self-driving vehicles. The Multi-Nested-Loop (MNL) Trajectory Tracking Control (TLC) strategy is adopted with the overall stability ascertained by linear-time-varying feedback control and the nonlinear singular perturbation principle. The assessment of Singular Perturbation Margin (SPM) in terms of phase margin of the unperturbed nominal system with stable zerodynamics is presented to demonstrate how stable zeros can provide additional SPM to the system, leading to practical benefits in reducing the time-scale separation in the MNL structure. The Trajectory Linearization Control approach entails combining an open-loop nonlinear dynamic (pseudo) inversion of the plant with a closed-loop tracking error feedback stabilizing controller, adhering to the premise that linearization is only valid within a limited error range. The proposed methods are verified through simulation and validated by hardware test on both sub-scale and full-scale vehicles, demonstrating the effectiveness in terms of tracking performance, low computational cost, robustness, scalability and ease of tuning. The validation process for the proposed system on a full-scale vehicle is conducted independently by an industrial company and certified beyond Technology Readiness Level (TRL) 5. Moreover, utilizing the proposed 3DOF baseline control algorithm, a vehicle rollover prevention control system is introduced by bandwidth adaptation. The vehicle roll dynamics are modeled as a parasitic singular perturbation. The rollover prevention control is integrated into the baseline 3DOF MNL TLC control algorithm. As a distinct feature of the rollover control, the yaw rate is used as control variable for stabilizing the roll motion, which do not req (open full item for complete abstract)

Committee: Jim Zhu (Advisor); Michael Braasch (Committee Member); David Drabold (Committee Member); Douglas Lawrence (Committee Member); Qiliang Wu (Committee Member); Chris Bartone (Committee Member) Subjects: Electrical 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, 2019, Engineering and Applied Science: Mechanical Engineering

Approximately 1.5 million senior citizens in the US live under nursing supervision, and most require assistance with at least one or more Activities of Daily Living (ADL) one of which is sit-to-stand (STS) transitions. The STS transition includes an inherent phase of instability which introduces a danger of falling for senior citizens and requires continuous supervision by healthcare workers or caregivers. Therefore, the development of assistive technologies to support human movements (i.e., exoskeletons, prostheses) has become a topic of increasing interest and urgency. Human motion is highly variable due to the effects of interaction with the environment and the intentionality of movement. Assistive robotic devices which aim to restore human motion need to account for and incorporate this variability in their operations. The aim of this dissertation is to analyze the dynamics of healthy STS transitions, present an approach to effectively plan STS trajectories, explore the efficacy of detecting intent of subsequent activity after STS, and analyze the effects of intent on the variability in human motion. Furthermore, the aim is to use the results obtained from the analysis of healthy STS transitions to develop a control strategy for exoskeletons which can exhibit the human-like variability behavior. In this work, an analysis of STS trajectories at different velocities and chair heights is presented which shows a clear correlation between the critical events (start of knee extension and time of weight transfer) and the way the momentum in modulated during the complete STS. Based on this, a model which approximates the velocities of the center of mass (CoM) in the vertical and horizontal directions and thus the whole-body momentum is presented and validated. The advantage of this model is that all the factors can be derived as a function of the total time required for STS. To analyze the effects of intent, an experiment with four subsequent actions of STS was des (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); Tamara Lorenz Ph.D. (Committee Chair); Adam Kiefer Ph.D. (Committee Member); Anca Ralescu Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Robots

PhD, University of Cincinnati, 2019, Engineering and Applied Science: Mechanical Engineering

Multi-UAV systems are inherently safety-critical systems, which means that safety guarantees must be made to ensure no undesirable configurations, such as collisions, occur. This dissertation focuses on developing optimization algorithms for trajectory planning of single as well as multiple cooperating Unmanned Air Vehicles (UAVs) operating in a cluttered environment that comprise of stationary obstacles and other cooperating as well as non-cooperating moving vehicles. This dissertation presents a Partial Differential Equation (PDE) based generalized method for UAV trajectory planning in a three-dimensional world using a number benchmark multi-UAV cooperative control problems. The PDEs proposed in this dissertation are based on the dynamics governing the multi-phase fluid motion in a porous medium. The method introduces a risk value representing the risk of collision or other hazard associated with every point in the domain. That risk value represents the notion of porosity (permeability) used in fluid flowing through a porous medium. This value is used in the PDE whose solution is obtained via novel numerical methods to calculate the streamlines that constitute the potential paths from a starting point to a target location. In particular, this research proposes a machine learning technique to decrease the computational time for calculations of flow movements in porous medium to 0.7 seconds which leads this technique to be implemented on-board and online. Subsequently, based on the criteria of the optimization problem, we propose post-processing of the streamlines to yield all the flyable paths. The proposed controller, based on multi-phase flows, is executed with a new decentralized manner using a concept of Prediction Sets (PSs). This method has been applied to three different cooperative control problems. IN first problem, large-scale path planning problem of UAVs is considered in shared airspace. The method is qualitatively compared via a simulation study (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); David Casbeer PhD (Committee Member); Kelly Cohen Ph.D. (Committee Member); Donald French Ph.D. (Committee Member); Tamara Lorenz Ph.D. (Committee Member) Subjects: Mechanical Engineering

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 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

Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering

Nonlinear systems are difficult to design a stabilizing and optimized control for, and with the increase of use of quadrotors by researchers, industry, and hobbyists, existing control methods should be analyzed to test the ability to control these devices. Many off-the-shelf quadrotors rely on onboard sensing and PID controllers to remain stable, however these devices are more commonly being used to perform fast obstacle avoidance and trajectory tracking, whose fast dynamics may not be trackable with PID controllers. We consider Linear Quadratic Regulator (LQR), iterative LQR, and Differential Flatness-based control methods to a quadrotor system to compare their performance to the onboard PID controllers. Considering a quadrotor used for tracking trajectories with a narrow corridor, the different controllers were designed and simulated using Matlab, applying interesting situations to test the robustness of the controllers, including noisy state measurements, delayed control action, and uncertainty in system parameters. In each scenario, the flatness-based control method is superior in robustly tracking the desired trajectory without requiring high computational power, possibly allowing the full control method to be implemented on a quadrotor microcontroller and improving the performance of the quadrotor's tracking abilities.

Committee: Wei Zhang (Advisor); Ran Dai (Committee Member) Subjects: Electrical Engineering