Mechatronics Systems Engineering - Theses, Dissertations, and other Required Graduate Degree Essays

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Novel Design of Energy Control Algorithm used in Solar Powered Batteryless Energy Harvesting System to power Wireless Sensor Node

Author: 
Date created: 
2019-11-28
Abstract: 

'Internet of Things' (IoT) technology is becoming one of the most important driving forces in human productivity in recent years.  New generation of 'super sensors', in the form of wireless sensor nodes (WSN) are the most important components in IoT. Powering these devices using traditional batteries creates a tough battery longevity problem, where the rigorous demands on the batteries require them to be replaced once every few years. This is further worsened by the huge number of devices in a typical IoT application, with their demand in power becoming a serious issue.   It is commonly considered that one of the best ways to power these wireless sensor nodes is to use energy harvesters with solar energy harvesting. Due to the unpredictable nature of solar irradiation, a problem to be solved is how a wireless sensor node powered by a solar energy harvester can have continual operation while simultaneously deliver the highest possible service duty.   This thesis presents a new energy control algorithm that addresses this bottleneck problem. Firstly, the analysis of past research using PID Control, Fuzzy Logic, and Adaptive Dynamic algorithm is provided, which reveals significant shortfalls.  The use of a solar irradiation prediction model by one group of researchers results in significant system shutdown (“dead time”) when actual solar irradiation deviates from the prediction model.  Another group of researchers maintain the terminal voltage of the supercapacitor at a certain set point but this approach is not able to avoid system shut down, and it demands an unacceptable operating condition in which certain amount of light must be present for the system to operate. After analysis of these past projects, the design deficits and imprecise design objectives in these researches are elaborated. Secondly, a proposal of a new energy control algorithm with the use of a precise two branch equivalent model is presented, with the employment of Model Predictive Control (MPC) theories to compute important control parameters. An augmented MPC control algorithm is designed based on three new principles, in order to handle the two mingled system input variables of system operating current and system sleep mode current of the WSN. Thirdly, the resulting new energy control algorithm is implemented in a self designed wireless sensor node embedded system. The purpose of this self designed system is to conduct comprehensive field tests to validate the performance and the robustness of the energy control algorithm. Finally, detail results with analysis of the four field tests is presented. The four field tests include the first test with normal operating condition, the second test as a stress test with an obstructed solar panel, the third as an additional stress test with a defective supercapacitor, and the fourth field test under abnormally adverse operating conditions. Except for the third field test which exhibits some time duration (2.8% of the total testing duration) with non-maximized WSN operation, all other field tests demonstrate full fulfillment of the new energy control algorithm’s design objectives. The last part of this thesis summarizes the conclusion of the research. And the research contribution in the field of IoT as well as in other numerous application areas are interpreted.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Zoë Druick
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

A Kinematic Rating System for evaluating helmet performance

Author: 
Date created: 
2019-12-05
Abstract: 

Adopting a helmet has been very helpful in reducing the risk of head injury in activities with a high risk of impact to the head. However, the main focus of most helmet standards is protecting the head against skull fracture through a pass or fail criterion that only measures the linear acceleration of the head during impact. Yet, it is known that most impacts result in both linear and rotational acceleration to the head. A pass-or-fail criterion does not inform the consumers how well a helmet performs. In recent years, Virginia Tech Summation of Tests for the Analysis of Risk (STAR) rating system was introduced to provide more insight into a helmet performance. The STAR rating system quantifies the risk of concussion based on the linear and rotational performance of a helmet. However, the science behind concussion is not fully understood, and in addition to helmet performance, the risk of concussion is closely related to other factors such as age, sex, genetic, the direction of an impact, and previous head trauma. The STAR rating also does not include all crucial factors in assessing a helmet performance, and therefore, it may not provide an accurate performance or risk of injury assessment for a given helmet. In this work, a Kinematic Rating System (KRS) was developed to evaluate helmet performance based on how well a helmet reduces crucial factors such as linear acceleration, rotational acceleration, and rotational velocity. KRS is an effective tool that provides an accurate assessment of the performance of a helmet compared to when the head is not protected by a helmet. KRS requires the helmet of interest to be tested against a 45º anvil at 6.5 m/s impact speed. Various football, hockey, and cycling helmets were tested according to the KRS, and the results were compared with the STAR rating system. In some cases, the performance reported by the STAR rating system were found to have significant discrepancies with the results obtained by the KRS. This is because the STAR rating system does not consider all the crucial factors while evaluating a helmet, such as the magnitude and duration of the acceleration pulse.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Farid Golnaraghi
Gary Wang
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Soft Gripper Driven by a Solenoid Actuator

Date created: 
2019-11-28
Abstract: 

In the past thirty years, robotics technology has become well-established in the manufacturing industry for reducing worker ergonomic stress and workload by performing operations such as picking and placing objects from a location to another, quickly, repetitively, and accurately. As we continue to integrate robots as versatile aids for industry, it is important to develop mechanisms that facilitate seamless cooperation between humans and robot assistants (RAs). Contributions of this thesis include the design and development of a more advanced, yet simple and cost-effective soft industrial robotic gripper that is scalable, and can be mounted on a wide range of commonly used robotic arms. The finished gripper prototype uses inexpensive components, and thus, would be economical to produce while addressing the needs of industry. Depending on the application, the developed gripper can outperform the state of the art in many “pick and place” tasks and is capable of picking up a wide variety of objects in size, weight, geometry and texture. To be applicable to current industrial warehouse environments, a series of tests were conducted to evaluate the effectiveness of the gripper in picking up and placing a set of items commonly available. The developed gripper in this work was mounted on a KUKA arm, and was tested for gripping objects from delicate ones such as a light bulb to heavier ones such as a 23 cm x 14 cm x 12 cm pack of eight cans of soda, weighing around 3 kg with a measured speed of 0.88 m/s.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Motion generation of a wearable hip exoskeleton robot using machine learning-based estimation of ground reaction forces and moments

Date created: 
2019-09-11
Abstract: 

Statistical data acquired from US citizens in 2013 showed that the overall percentage of all disabilities for all ages in this country was around 12.6%, in which the “ambulatory disabilities” had the highest prevalence rate (7.1 %). This amount is estimated around 7.2% for all Canadian adults, which corresponds to more than 2.5 million people. In order to improve the quality of life of those with ambulatory disabilities (e.g., paraplegic people), wearable robotic exoskeleton is being developed in our lab. In this project, Ground Reaction Forces and Moments (GRF/M), which are important data for closed-loop control of an exoskeleton, is estimated based on lower limb motion of a wearable hip exoskeleton user. This method can reduce manufacturing cost and design complications of these types of robots. In order to model GRF/M, Neural Network, Random Forest and Support Vector Machine algorithms are utilized. Afterward, the achieved results from the three algorithms are compared with each other and some of the most recent similar studies. In the next step, the trained models are employed in an online control loop for assisting a healthy exoskeleton user to walk easier. The device applies forces on the user’s upper thigh, which reduces the required torque of the hip flexion-extension joint for the user. Finally, the exoskeleton’s performance is compared experimentally with the cases when the device is not powered or it is simply following the user’s motion based on the inverse kinematics. The results demonstrate that the presented algorithm can help the exoskeleton user to walk easier.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Siamak Arzanpour
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Design and development of a wearable inductive textile sensor to monitor back movements

Date created: 
2020-11-18
Abstract: 

This thesis focuses on the design and development of a wireless and wearable platform that employs an inductive sensor to track trunk movements when the user bends forward. The inductive textile sensor was designed based on the anthropometrical dimensions of the trunk’s lumbar area of a healthy female. The chosen shape of the sensor was a rectangular flat coil. The inductance behavior was investigated using theoretical calculations and simulations. Formulas developed by Grover and Terman were used to calculate the inductance to validate the inductive textile design. The simulations were used to analyze the change of the inductance when the area, perimeter, height, and width of the rectangle was modified, as well as the effect of the number of turns of the rectangular flat coil. Results from the theoretical calculations and simulations were compared. The inductive textile sensor was integrated at the lumbar section of a sleeveless garment to create a smart wearable platform. The performance of the smart garment was evaluated experimentally on a healthy participant, and it was shown that the designed sensor can detect forward bending movements. The evaluation scenario was further extended to also include twisting and lateral bending of the trunk, and it was observed that the proposed design can successfully discriminate such movements from forward bending of the trunk. An interference test showed that, although moving a cellphone towards the unworn prototype affected the sensor readings, manipulating the cellphone when wearing the prototype, did not compromise the capability of the sensor to detect forward bends. The proposed platform is a promising step towards developing wearable systems to monitor back posture to prevent or treat low back pain associated with poor posture.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Design of a bidirectional energy buffer using a switched-capacitor converter and supercapacitors for an auxiliary EIS converter for fuel cell stacks

Author: 
Date created: 
2020-10-21
Abstract: 

Fuel cell as an attractive clean energy source has gained a great deal of interest. To increase the durability and reliability of fuel cells, diagnostics systems that can detect degradation and faults inside fuel cell stacks in end applications are highly in need. Electrochemical impedance spectroscopy (EIS), among other methods, is a promising characterizing tool for diagnostics and condition monitoring of fuel cells. It was traditionally only applied to single-cell or short stacks at low-power levels and required special laboratory equipment, but was recently brought to high-power stacks too which was made possible by many technological advancements. This is mainly owing to a growing interest in performing in situ EIS as a non-destructive method without the need for dismantling the stack. Unlike traditional approaches which relied on extra equipment, converter-based EIS provides attractive solutions for this purpose. In this thesis, the design and utilization of a bidirectional energy buffer module composed of a switched-capacitor converter (SCC) and a supercapacitor string for a new auxiliary EIS converter solution is presented. The module is designed towards having a more compact auxiliary converter unit. The design of the proposed energy buffer module is investigated in detail and a guideline is provided considering the application-specific optimal conversion ratio, supercapacitor string capacitance, and the probable limitations imposed by high EIS frequencies on certain situations. In a nutshell, the proposed switched-capacitor converter module (SCCM) consists of a bidirectional high voltage-gain SCC connected with supercapacitor string helps with the compactness and miniaturization of the entire auxiliary EIS converter and eliminating the potential problems of electrolytic capacitors such as bulkiness and limited lifetime due to the impact of ripples. The SCCM energy buffer with a high voltage gain offers a high buffering ratio for utilizing supercapacitors as the energy storage device.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Jiacheng (Jason) Wang
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

PEMFC performance improvement through oxygen starvation prevention, modeling, and diagnosis of hydrogen leakage

Author: 
Date created: 
2020-10-13
Abstract: 

Catalyst degradation results in emerging pinholes in Proton Exchange Membrane Fuel Cells (PEMFCs) and subsequently hydrogen leakage. Oxygen starvation resulting from hydrogen leaks is one of the primary life-limiting factors in PEMFCs. Voltage reduces as a result of oxygen starvation, and the cell performance deteriorates. Starved PEMFCs also work as a hydrogen pump, increasing the amount of hydrogen on the cathode side, resulting in hydrogen emissions. Therefore, it is important to delay the occurrence of oxygen starvation within the Membrane Electrode Assembly (MEA) while simultaneously be able to diagnose the hydrogen crossover through the pinholes. In this work, first, we focus on catalyst configuration as a novel method to prevent oxygen starvation and catalyst degradation. It is hypothesized that the redistribution of the platinum catalyst can increase the maximum current density and prevent oxygen starvation and catalyst degradation. Therefore, a multi-objective optimization problem is defined to maximize fuel cell efficiency and to prevent oxygen starvation in the PEMFC. Results indicate that the maximum current density rises about eight percent, while the maximum PEMFC power density increases by twelve percent. In the next step, a previously developed pseudo two-dimensional model is used to simulate fuel cell behavior in the normal and the starvation mode. This model is developed further to capture the effect of the hydrogen pumping phenomenon and to measure the amount of hydrogen in the outlet of the cathode channel. The results obtained from the model are compared with the experimental data, and validation shows that the proposed model is fast and precise. Next, Machine Learning (ML) estimators are used to first detect whether there is a hydrogen crossover in the fuel cell and second to capture the amount of hydrogen cross over. K Nearest Neighbour (KNN) and Artificial Neural Network (ANN) estimators are chosen for leakage detection and classification. Eventually, a pair of ANN classifier-regressor is chosen to first isolate leaky PEMFCs and then quantify the amount of leakage. The classifier and regressor are both trained on the datasets that are generated by the pseudo two-dimensional model. Different performance indexes are evaluated to assure that the model is not underfitting/overfitting. This ML diagnosis algorithm can be employed as an onboard diagnosis system that can be used to detect and possibly prevent cell reversal failures.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Krishna Vijayaraghavan
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) Ph.D.

Development of novel sorber bed heat and mass exchangers for sorption cooling systems

Date created: 
2020-09-09
Abstract: 

The current cooling systems mainly employ vapor compression refrigeration technology, which increases the electricity peak load significantly and has a high carbon footprint. One alternative solution is sorption systems, run by low-grade thermal energy, i.e. heat sources with temperature less than 100 ºC, such as waste heat, which is non-payable. Also, sorption systems have negligible carbon footprint. Despite all the promising features and benefits, current sorption systems are not ready for wide market adoption. A revolutionary approach to their design and development is needed to overcome their technical limitations such as low specific cooling power (SCP) and low coefficient of performance (COP). Graphite flakes were added to the sorbent to increase the sorbent thermal diffusivity; however, it reduces the active sorbent. The counteracting effect of graphite flake additives in the sorbent was studied using a custom-built gravimetric large pressure jump test bed. It was found that graphite flake additives can increase or decrease the sorption performance depending on the cycle time. Furthermore, 2-D analytical models were developed that consider the spatial and temporal variation of water uptake and temperature in sorber bed heat and mass exchangers (S-HMXs). Two designs of plate fin (P-HMX) and finned-tube (F-HMX) were considered because of the high SCP and COP. Using the analytical models, it was shown that the entire S-HMX components should be optimized simultaneously, and the objective functions of SCP and COP should be optimized together. Thus, an analysis of variance and simultaneous multi-objective optimization of the S-HMX components were performed using the developed analytical models. Based on the optimization study, the P-HMX and the F-HMX were specifically designed and built for sorption cooling systems. The experimental results showed that the present P-HMX achieved an SCP of 1,005 W/kg sorbent, and a COP of 0.60 for Tdes=90 °C, Tsorp= Tcond=30 °C and Tevap=15 °C. Furthermore, the F-HMX yielded an SCP of 766 W/kg and COP of 0.55. It was shown that the P-HMX provided 4.3 times higher SCP, and 3 times higher COP compared to an off-the-shelf heat exchanger coated with a similar composite sorbent consisting of CaCl2, silica gel B150 and PVA.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Majid Bahrami
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) Ph.D.

4D in situ visualization of chemo-mechanical membrane degradation in fuel cells: Understanding and mitigating edge failures

Author: 
Date created: 
2020-08-17
Abstract: 

Fuel cell is a zero-emission energy conversion device using hydrogen and oxygen to generate power with water as the only by-product. Membrane electrode assembly (MEA) edges are sensitive regions that could influence the overall durability of fuel cells, where membrane degradation at poorly designed edges may lead to premature cell failures. In this work, two MEA edge designs were implemented to study their robustness during combined chemical and mechanical accelerated stress testing. Four-dimensional in situ visualization, enabled by X-ray computed tomography, was performed to understand and mitigate the edge failure issue. Interaction of adhesive-containing polyimide gasket with catalyst coated membrane (CCM) was identified as the key contributor to premature edge failures, which was mitigated by using a non-adhesive inert frame at the CCM interface, thus enabling a robust MEA edge wherein the failures were shifted into the active area. Overall, findings of this research may contribute to robust fuel cell manufacturing and enhanced membrane durability.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Erik Kjeang
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Towards the vision of a social robot in every home: A navigation strategy via enhanced subsumption architecture

Author: 
Date created: 
2020-06-29
Abstract: 

In this thesis, we report the studies undertaken in the design and implementation of a behavioristic navigation system for social robots with limited sensors to be deployed in family homes. The project was completed in four phases. Each phase of the project was independently evaluated in virtual or real-time implementation on the NAO humanoid robot. In the first phase of this research study, we address the problem of indoor room classification via several convolutional neural network (CNN) architectures. The main objective was to recognize different rooms in a family home. We also propose and examine a combination model of CNN and a multi-binary classifier referred to as Error Correcting Output Code (ECOC). In the second phase, we propose a new dataset referred to as SRIN, which stands for Social Robot Indoor Navigation. This dataset consists of 2D colored images for room classification (termed SRIN-Room) and doorway detection (termed SRIN-Doorway). The main feature of the SRIN dataset is that its images have been purposefully captured for short robots (around 0.5-meter tall). The methodology of collecting SRIN was designed in a way that facilitated generating more samples in the future regardless of where the samples have come from. In phase three, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. Finally, we report an end-to-end navigation system for social robots in family homes. The system combines a reactive-based system and a knowledge-based system with learning capabilities in a meaningful manner for social robot applications.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ahmad Rad
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) Ph.D.