The aim of this thesis is to study adaptive controllers in the context of a Pro-portional Integral Derivative (PID) controller. The PID controller is tuned via loop shaping techniques to ensure desired robustness and performance characteristics with respect to a target loop shape. There are two problems that this work addresses: Consider a system that is controlled via an adaptive PID controller. If in absence of or under lack of excitation, the system or controller parameters drift to an arbitrary system (that may or may not be stable). Then, once the system gets sufficient ex- citation, there are two questions to be addressed: First, how quickly is the system able to recover to the target system, and in the process of recovery, how large are the transient overshoots and what factors affect the recovery of the drifted system? Second, continuous online adaptation of the controller may not always be necessary (and economical). So, is there a means to monitor the performance of the current controller and determine via robustness conditions whether to continue with the same controller or reject it and adapt to a new controller? Hence, this work is concerned with robust performance monitoring and recovery of an adaptive PID control system that had drifted to another system in absence of sufficient excitation or excessive noise.

This work endeavors to lay a solid foundation for the exploration and the considerations of exoskeletons, exosuits, and medical devices concerning proprioceptive feedback. This investigation is situated at the nexus of engineering, neuroscience, and rehabilitation medicine, striving to cultivate a holistic understanding of how mechanical augmentation, interfaced synergistically with human proprioception, can foster enhanced mobility and safety. This is especially pertinent for individuals with compromised motor functions.British Neurologist Oliver Wolf Sacks in 1985 published “The Man who Mistook His Wife for a Hat” a series of his most memorable neurological case describing the brain's strangest pathways. One of these cases is “The Disembodied Lady”, Christina a 27-year-old woman that lost entirely the sense of proprioception due to polyneuropathy. This caused her to not be able to control her body, and she declares that “I feel the wind on my arms and face, and then I know, faintly, I have arms and a face. It’s not the real thing, but it’s something—it lifts this horrible, dead veil for a while. ” Finally, she was able to control her body using vision alone. Dr. Sacks introduced, for the first time, the importance of proprioception, as the sense of position of body parts relative to other parts of the body, to western culture. This document’s mission is to identify unexplored concepts in the literature regarding exoskeletons, wearables and assistive technology and a user’s proprioception, embodiment and utilization when wearing devices. Dr. Philipp Beckerle suggests the need to research the connections between wearable hardware and human sense of proprioception. He also emphasizes the need for functional assessment protocols for wearables devices and the role of embodiment. He criticizes the current commercially available upper-limb prostheses since they only restore limited functions and therefore impede embodiment. This document’s goal is to identify operative solutions through the adaptation of existing technologies and to use effective solutions to improve the quality of life of people suffering from pathologies or traumatic injuries.

Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms. Thus, incentivizing collaboration and preventing collisions are the two principles which are followed by the agent during the training process. Nowadays, more and more applications, both in industry and daily lives, require at least two arms, instead of requiring only a single arm. A dual-arm robot satisfies much more needs of different types of tasks, such as folding clothes at home, making a hamburger in a grill or picking and placing a product in a warehouse. The applications done in this paper are all about object pushing. This thesis focuses on how to train the agent to learn pushing an object away as far as possible. Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then utilized in this paper to train the agent to generate optimal actions. Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL methods used in this thesis.

This research proposes some new data-driven control methods to control a nonlinear dynamic model. The nonlinear dynamic model linearizes by using the Koopman theory. The Koopman operator is the most important part of designing the Koopman theory. The data mode decomposition (DMD) is used to obtain the Koopman operator. The proposed data-driven control method applies to different nonlinear systems such as microelectromechanical systems (MEMS), Worm robots, and 2 degrees of freedom (2 DoF) robot manipulators to verify the performance of the proposed method. For the MEMS gyroscope, three control methods are applied to the linearized dynamic model by the Koopman theory: linear quadratic regulator (LQR), compound fractional PID sliding mode control, and fractional order PID controller tuned with bat algorithm. For the Worm robot, an LQR controller is proposed to control the linearized dynamic model by the Koopman theory. A new fractional sliding mode control is proposed to control the 2 DoF arm robot. All the proposed controllers applied to the linearized dynamic model by the Kooman theory are compared with some conventional proposed controllers such as PID, sliding mode control, and conventional fractional sliding mode control to verify the performance of the proposed controllers. Simulation results validate their performance in high tracking performance, low tracking error, low frequency, and low maximum overshoot.

Photoplethysmography (PPG) is a noninvasive optical signal that measures the change in blood volume. This particular signal can be interpreted to yield heart rate (HR) information which is commonly used in medical settings and diagnostics through wearable devices. The noninvasive nature of the measurement of the signal however causes it to be susceptible to noise sources such as motion artifacts (MA). This research starts by describing an end-to-end embedded HR estimation system that leverages noisy PPG and accelerometer data through machine learning (ML) to estimate HR. Through embedded ML for HR estimation, the limitations and challenges are highlighted, and a different HR estimation method is proposed. Next, a point-based value iteration (PBVI) framework is proposed to optimally select HR estimation filters based on the observed user activity. Lastly, the underlying dynamics of the PPG are explored in order to create a sparse dynamic expression of the PPG signal, which can be used to simulate PPG data to improve ML or remove MA from PPG.

In today’s modern world, industrial robots are utilized in hazardous working condi-tions across all industries, including the renewable energy industry. Robot control systems and sensors receive and transmit information and data obtained from the users. Over the last ten years, unmanned vehicles have developed into a subject of interest for a variety of research institutions. Technology breakthroughs are redefin- ing disaster relief, search-and-rescue(SAR) and salvage operations’ for aerial robotic systems as well as terrestrial and marine ones. A team of collaborative robots is required for the challenging environments, such as space construction, and disaster relief. These robots will have to make trade-offs between mobility and capabilities owing to cost, power, and size constraints. Task execution in numerous areas may de- mand for robot collaboration in order to optimize team performance. An analysis of collaborative Unmanned Aerial Vehicle(UAV) and Unmanned Ground Vehicle(UGV) systems is one of the main components of this thesis. UAV/UGV collaborative frame- works and methods have been presented for reaching or monitoring moving human targets, a stated set-point for a mobile UGV robot to go to in order to approach a dynamic target, and actions to take by the UAVs when the mobile UGV robot is obstructed and cannot reach the target. This method encourages the target and robot to work together more closely. This is one of the most difficult issues in search and rescue operations since human targets are seldom found using just land robots or aerial robots. Finally, the purpose of this thesis is to suggest that the evaluation of the performance of a collaborative robot system may be accomplished by measuring the mobility of robots. Even though multi-robot coordination aids in SAR opera- tions, the findings of the study presented in this thesis conclude that the integration of various autonomous robotic systems in unstructured environments is difficult and that there is currently no unitary analytical model that can be used for this purpose.

This document is the culmination of research into small unmanned Powered Parachute aerial vehicles. This dissertation serves to provide designers of small systems with an approach to developing a Powered Parachute Unmanned Aerial Vehicle system, guiding them through the basic assumptions, dynamics, and control method. In addition, this dissertation aims to generate a reliable and generalized framework of dynamic design and control methods for autonomous Powered Parachute aircraft. The simulation methods in this paper assist in developing a consistent and robust unmanned system for applying Powered Parachutes as an alternative to multirotor or fixed-wing aircraft.The first chapter serves as a primer on the historical applications of small Unmanned Systems and Powered Parachutes and gives an overview of the requirements for building an autonomous Powered Parachutes; the information within this chapter provides justification background for the second chapter on Powered Parachute dynamics. In the dynamics chapter, equations of motion are derived using engineering first principles. This chapter also discusses alternative methods of improving the control and robustness of the Powered Parachute airframe. The dynamics model is used in all further chapters to develop a generalized control system to operate such a model autonomously. Chapter three of this document focuses on developing simulations from the dynamics described in the previous chapter, laying the groundwork for guidance, navigation, and control algorithms ahead. Chapters four and onwards refine the autonomous control of the Powered Parachute aircraft for real-world scenarios, discussing correction factors and minimizing the errors present in current sensor systems. Chapter five covers the development of an additional adaptive controller which uses a Sigma-Pi Neural network integrated into the final control loop. Chapter six develops advanced control methods for the Powered Parachute airframe, including simulations on a novel proposed thrust vectoring method. Finally, chapter seven discusses results accumulated from testing an experimental prototype.

Photoplethysmography (PPG) is currently a leading and growing field of researchwithin the biomedical industry. With its primary use in pulse oximetry and capability of quickly, non-intrusively, evaluating essential vital signs like heart rate and oxygen levels. This thesis will explore the literature on new and innovative research in pulse oximetry. Then introduce PPG signals including how to calculate heart rate, oxygen saturation, and current problems, mainly focused on motion artifacts. The development of hardware and software systems using Bluetooth to transmit data to MATLAB for algorithm processing. Testing different signal processing techniques and parameters evaluating their effects on algorithm accuracy and reduction of motion artifact. Using accelerometers to identify motion and apply filters to effectively reduce minor motion artifacts. Then perform real-time data analysis and algorithm processing resulting in heart rate and oxygen level calculations.

There has been a decrease in the fertility rate over the years due to today’s younger generation facing more pressure in the workplace and their personal lives. With an aging population, more and more older people with limited mobility will require nursing care for their daily activities. There are several applications for wearable sensor networks presented in this paper. The study will also present a motion capture system using inertial measurement units (IMUs) and a pressure-sensing insole with a control system for gait assistance using wearable sensors. This presentation will provide details on the implementation and calibration of the pressure-sensitive insole, the IMU-based motion capture system, as well as the hip exoskeleton robot. Furthermore, the estimation of the Ground Reaction Force (GRF) from the insole design and implementation of the motion tracking using quaternion will be discussed in this document.

While pulse oximeter technology is not necessarily an area of new technology, advancements in performance and package of pulse sensors have been opening up the opportunities to use these sensors in locations other than the traditional finger monitoring location. This research report examines the full potential of creating a minimally invasive physiological and environmental observance method from the ear location. With the use of a pulse oximeter and accelerometer located within the ear, there is the opportunity to provide a more in-depth means to monitor a pilot for a Gravity-Induced Loss of Consciousness (GLOC) scenario while not adding any new restriction to the pilot's movement while in flight. Additionally, building from the GLOC scenario system, other safety monitoring systems for military and first responders are explored by alternating the physiological and environmental sensors. This work presents the design and development of hardware, signal processing algorithms, prototype development, and testing results of an in-ear wearable physiological sensor.