Master of Science, The Ohio State University, 2013, Computer Science and Engineering

According to a recent report by the US Treasury Department, America wastes $8 billion annually in energy costs because of traffic congestion. Adding the cost of lost time, the damage is said to reach around $100 billion. Moreover, high energy consumption adds to air pollution and contributes to the global warming problem. Infrastructure where different entities (cars and traffic signals) can communicate with each other offers the potential for reducing this waste. But by how much? Suppose full information about location, velocity, and acceleration of each vehicle were available for all vehicles in the vicinity of an isolated traffic signal. Could an intelligent traffic signal predict and adjust to the best possible traffic light cycle times to minimize fuel loss per vehicle? If light timing were changed dynamically based on real-time information from new traffic arrivals after a small interval of time, how much lower fuel loss could be achieved than by basing timing on macro-level metrics such as flow rates and limited vehicle information such as that provided by in-pavement loop detectors? Answering these questions involves developing a simulation framework that is based on an understanding of typical yet safe vehicle operation (by human drivers or autonomous vehicles) and of various traffic arrival patterns, as well as the ability to estimate fuel loss (and/or other optimization objectives) in many different situations.

Committee: Bruce Weide (Advisor); Paul Sivilotti (Committee Member) Subjects: Computer Engineering; Computer Science; Transportation; Transportation Planning

Master of Science in Electrical Engineering, University of Dayton, 2022, Electrical Engineering

Despite impressive advancements in the field of robotics, tasks such quick reaching movements, bipedal locomotion, and balance maintenance have shown to be a challenge. A possible reason for this is the predominance of feedback controls in robotics, which provide robust controllers at the expense of a slower response. The part of the human brain responsible for the performance of such tasks is the cerebellum, which functions exclusively in a feedforward way. Prior studies have shown cerebellum inspired controller's capabilities in movement learning, performing quick reaching movements, and functioning in uncertain environments. This thesis focuses on supervised learning cellular-level cerebellum computational models and its capability of performing balance related tasks. Through computer simulations, the innovative design was tested for the first time on the balancing of the inverted pendulum and double inverted pendulum. Another concept investigated in this work is the effect of cerebellum network size on performance, where among four different network sizes, the largest network ever simulated by the EDLUT spiking neural network simulator was created. Lastly, the controller's capability to transfer knowledge to another model performing the same task with different dynamics was evaluated. All controller sizes tested displayed impressive results on the inverted pendulum, quickly learning how to balance the pole. For the double inverted pendulum, all but the smaller sized network were able to achieve learning. The larger networks displayed better performance in both tasks, but the creation of even larger networks might be necessary to properly define the cerebellum network size effect on performance. The bio-inspired design was also shown to be capable of transferring knowledge, with an initially trained controller outperforming an initially naive controller on inverted pendulum models with different dynamics. The findings of this experiment show that cerebellum comp (open full item for complete abstract)

Committee: Raúl Ordóñez (Advisor); Terek Taha (Committee Member); Temesguen Kebede (Committee Member) Subjects: Electrical Engineering

Master of Science in Mechanical Engineering, Cleveland State University, 2021, Washkewicz College of Engineering

Advancements in control systems and optimization can potentially be used to enhance the performance of exoskeletons and prostheses in various aspects, such as to improve balance control and gait adaptation. These are the two aims of this thesis. Aim 1 is to improve the balance of an underactuated exoskeleton with full-state feedback. The exoskeleton was modeled as a three-link inverted pendulum with passive stiffness at the ankle and controlled actuations at its other two joints. Though the system has no controlled actuation at its pivot, the system could be stabilized at its equilibrium point by a linear quadratic regulator (LQR) at the other two actuated joints to maintain the upright position against small perturbations. The feedback control parameters were then optimized to further improve the stability of the system. Aim 2 is to improve the gait adaptation in exoskeletal walking. A control strategy based on human-in-the-loop optimization is presented in this thesis. This controller allows the exoskeleton to adapt to the changes in gait pattern and walking speed by optimization of the cost function based on muscle activation and ground reaction force. Simulation and real-time test experimental results of this adaptive controller are shown in this thesis.

Committee: Antonie van den Bogert (Committee Chair); Eric Schearer (Committee Member); Ryan Farris (Committee Member) Subjects: Engineering; Mechanical Engineering; Robotics

MS, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

The thesis focusses on formulating novel control algorithm architectures based off the sliding mode controller design. Three control designs are presented in this thesis. The three control architectures are tested on a quadrotor and their behaviour is observed when waypoint navigation is attempted. The first control architecture is to develop a control design that is effective against external disturbances. The theoretical design and working of the controller is first presented. Its ability to stabilize systems in the presence of disturbance is also presented theoretically. Consequently, to test its effectiveness, its reaction to wind and gust disturbance is observed when implemented on a quadrotor. The second and third control architecture deals with a certain type of controller known as the PD (Proportional Derivative) controller. The PD controller helps us control the dynamics of a system in a simple manner. However, in order to do so in a successful manner, there are certain parameters in its structure that need to be decided manually. This can be a difficult task for larger dynamical systems. The second and third control architectures present two methods in which these gains automatically take ideal values which help channel system dynamics. This rules out the need for determining them manually. A theoretical proof for this method is presented. Simulations to understand the working of the idea are also shown. Conclusions for the 3 control architectures are drawn out. Future work is also presented.

Committee: Manish Kumar Ph.D. (Committee Chair); Rajnikant Sharma Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

Microcontroller Units (MCUs) are central and essential to many consumer electronic and industrial applications, including communication systems, automotive, and Internet of Things (IoT). Since, these MCUs can be used in various applications with different operating conditions, designing the internal power supply of such MCUs is quite challenging. For example, in some applications the MCUs could be powered from a Li-ion battery while in other application it could be powered from on-board regulator, or even an AC-to-DC adapter. This indeed requires the internal power supply of such MCUs to handle a very wide range of input voltages. In addition, these MCUs typically contains analog and digital circuits that operates from different supply levels. As a result, the internal power supply of the MCU has also to support a wide range of output voltage instead of designing separate power supply for each block which requires additional design and layout efforts. Moreover, depending on the performance requirements of the MCU or the mode of operation, the current consumption can vary very widely. It can be as high as 150-300 mA in active and high performance mode or it can be as low as 10-200 µA in sleep or idle mode. Consequently, the internal power supply of the MCU has to support a wide range of load currents. It is important to mention that since MCUs usually stay more than 50% of their time in sleep mode, the efficiency of their internal power has to be high not only in active mode (heavy load condition), but also in sleep mode (ultra-light load condition). Furthermore, each application puts different limitations and constrains on the passives (i.e. inductors or capacitors) used with the MCU. This includes different size and cost which exaggerate the constrains of the MCU's internal power supply which has to support a very wide range of passive components as well. Most importantly, since some low-noise MCUs usually contain noise sensitive IPs such as PLLs, Oscillators, and (open full item for complete abstract)

Committee: Ayman Fayed (Advisor); Patrick Roblin (Committee Member); Steven Bibyk (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2016, Mechanical Engineering

UAVs have become increasingly popular around the world both in civilian and military application within the past few years. UAV applications are essentially fueled by advances in a combination of technologies, such as communication, embedded systems, processing, sensing and algorithms. This thesis focuses on the control aspect of UAVs, particularly lots of work has been carried out in this area recently. This thesis mainly focuses on the performance of control algorithms under wind disturbances. Recent works that include an adaptive controller and a PD controller are discussed. Then the thesis proposes a neural PD controller, in which the neural network is added to the outer PD control loop. A comparison between neural PD controller and PD controller under various scenarios of wind disturbances is carried out. The results show neural PD controller performs better than the PD controller in position tracking under wind disturbances.

Committee: Manish Kumar (Committee Chair); Mohammad Elahninia (Committee Member); Abdollah Afjeh (Committee Member) Subjects: Mechanical Engineering

Master of Science, University of Akron, 2012, Mechanical Engineering

This work deals with the complex task of unscrewing and screwing a threaded bottle cap with a dexterous anthropomorphic robotic hand in two cases: i.e, with the thumb-first finger and also with the thumb-little finger. To that end, human motion profiles of nine test subjects were recorded while unscrewing and screwing a bottle cap with five different orientations of their hand with respect to the bottle. Results showed that the periodic motions exhibited by the finger joints shared a common frequency for each subject, but differed in amplitude and phase. From the gathered data, a set of sinusoidal trajectories were developed to approximate this motion for a robotic hand. Because the joint trajectories share the same frequency, a family of sinusoidal inputs can be used in the path planning of the robot to unscrew and screw threaded objects. This significantly reduces the computational cost and complexity of the task. Additionally, the unscrewing data appears highly similar to the mirror image of the screwing data. This implies that the transition to or from screwing or unscrewing motions can be achieved simply by increasing or decreasing the time vector within the family of sinusoids. Simulation results show that the developed sinusoidal trajectories show a close correlation with the motion profiles seen from human experiments. Furthermore, this solution is broadened to two cases. Case1: objects with wide variations in diameters by relating joint angle offsets of the hand to diameter size through the forward kinematics equations. Additional experiments are performed with different object diameters to show the versatility of the concept. The sinusoidal trajectories are all implemented within a PID sliding mode controller to ensure overall system stability. Using the developed sinusoidal joint angle trajectories, the robotic hand successfully unscrewed and screwed four different objects in all trials conducted with each object diameter size. Case2: An adaptive synergy (open full item for complete abstract)

Committee: Erik Engeberg Dr. (Advisor); Abhilash Chandy Dr. (Committee Member); Jiang Zhe Dr. (Committee Member) Subjects: Biomedical Engineering; Mechanical Engineering; Robotics