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- Creators: Barrett, The Honors College
- Member of: Theses and Dissertations
Description
This thesis project examines the stability margin for different rotor configurations for a quadcopter and compares them against each other to determine the most stable flight configuration possible. The first configuration develops a “standard” for quadcopters with each motor in a corner of a cube at a 60-degree angle from the Y-Axis. The remaining tests increase the angle five degrees per configuration, allowing the motors to get incrementally closer to each other until no longer viable. Five different tests are outlined below depicting the microscopic changes in the pitch and roll of the device. The on-board controller in the quad-copter tracks both the acceleration and gyroscopic movements of the device to obtain the stability margin of each test. Computational analysis is then used to calculate and compare the values found to determine the most stable configuration.
ContributorsCorino, Tyler Michael (Author) / Kuo, Chen-Yuan (Thesis director) / Lynch, John (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
For my thesis I worked in ASU’s Bio-Inspired Mechatronics lab on a project lead by PhD student Pham H. Nguyen (Berm) to develop an assistive soft robotic supernumerary limb. I contributed to the design and evaluation of two prototypes: the silicon based Soft Poly Limb (SPL) and one bladder-based fabric arm, the fabric Soft Poly Limb (fSPL). For both arms I was responsible for the design of 3D printed components (molds, end caps, etc.) as well as the evaluation of the completed prototypes by comparing the actual performance of the arms to the finite element predictions. I contributed to the writing of two published papers describing the design and evaluation of the two arms. After the completion of the fSPL I attempted to create a quasi-static model of the actuators driving the fSPL.
ContributorsSparks, Curtis Mitchell (Author) / Sugar, Thomas (Thesis director) / Zhang, Wenlong (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Arizona has been rapidly expanding in both population and construction over the last 20 years, and with the hot summer climate, many homeowners experience a significant increase in their utility bills. The cost to reduce these energy bills with home renovations can become expensive. This has become increasingly apparent over the last few years with the impact that covid had on the global supply chain. Prices of materials and labor have never been higher, and with this, the price of energy continues to increase. Therefore, it is important to explore methods to make homes more energy-efficient without the price tag. In addition to benefitting the homeowner by decreasing the cost of their monthly utility bills, making homes more energy efficient will aid in the overall goal of reducing carbon emissions.
ContributorsFiller, Peyton (Author) / Phelan, Patrick (Thesis director) / Parrish, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05
Description
Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.
ContributorsQIAO, ZHI (Author) / Zhang, Wenlong (Thesis advisor) / Marvi, Hamidreza (Committee member) / Lee, Hyunglae (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.
Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
The concept of entrainment broadly applies the locking of phases between 2 independent systems [17]. This physical phenomenon can be applied to modify neuromuscular movement in humans during bipedal locomotion. Gait entrainment to robotic devices have shown great success as alternatives to labor intensive methods of rehabilitation. By applying additional torque at the ankle joint, previous studies have exhibited consistent gait entrainment to both rigid and soft robotic devices. This entrainment is characterized by consistent phase locking of plantarflexion perturbations to the ‘push off’ event within the gait cycle. However, it is unclear whether such phase locking can be attributed to the plantarflexion assistance from the device or the sensory stimulus of movement at the ankle. To clarify the mechanism of entrainment, an experiment was designed to expose the user to a multitude of varying torques applied at the ankle to assist with plantar flexion. In this experiment, no significant difference in success of subject entrainment occurred when additional torque applied was greater than a detectable level. Force applied at the ankle varied from ~60N to ~130N. This resulted in successful entrainment ~88\% of the time at 98 N, with little to no increase in success as force increased thereafter. Alternatively, success of trials decreased significantly as force was reduced below this level, causing the perturbations to become undetectable by participants. Ultimately this suggests that higher levels of actuator pressure, and thus greater levels of torque applied to the foot, do not increase the likelihood of entrainment during walking. Rather, the results of this study suggest that proper detectable sensory stimulus is the true mechanism for entrainment.
ContributorsKruse, Anna (Author) / Lee, Hyunglae (Thesis director) / Berman, Spring (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-12