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Title: Natural language based control and programming of robotic behaviors
Creator: Cheng, Yu (Graduate of Michigan State University)
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Robots have been transforming our daily lives by moving from controlled industrial lines to unstructured and dynamic environments such as home, offices, or outdoors working closely with human co-workers. Accordingly, there is an emerging and urgent need for human users to communicate with robots through natural language (NL) due to its convenience and expressibility, especially for the technically untrained people. Nevertheless, two fundamental problems remain unsolved for robots to working...
Show more"Robots have been transforming our daily lives by moving from controlled industrial lines to unstructured and dynamic environments such as home, offices, or outdoors working closely with human co-workers. Accordingly, there is an emerging and urgent need for human users to communicate with robots through natural language (NL) due to its convenience and expressibility, especially for the technically untrained people. Nevertheless, two fundamental problems remain unsolved for robots to working in such environments. On one hand, how to control robot behaviors in dynamic environments due to presence of people is still a daunting task. On the other hand, robot skills are usually preprogrammed while an application scenario may require a robot to perform new tasks. How to program a new skill to robots using NL on the fly also requires tremendous efforts. This dissertation tries to tackle these two problems in the framework of supervisory control. On the control aspect, it will be shown ideas drawn from dynamic discrete event systems can be used to model environmental dynamics and guarantee safety and stability of robot behaviors. Specifically, the procedures to build robot behavioral model and the criteria for model property checking will be presented. As there are enormous utterances in language with different abstraction level, a hierarchical framework is proposed to handle tasks lying in different logic depth. Behavior consistency and stability under hierarchy are discussed. On the programming aspect, a novel online programming via NL approach that formulate the problem in state space is presented. This method can be implemented on the fly without terminating the robot implementation. The advantage of such a method is that there is no need to laboriously labeling data for skill training, which is required by traditional offline training methods. In addition, integrated with the developed control framework, the newly programmed skills can also be applied to dynamic environments. In addition to the developed robot control approach that translates language instructions into symbolic representations to guide robot behaviors, a novel approach to transform NL instructions into scene representation is presented for robot behaviors guidance, such as robotic drawing, painting, etc. Instead of using a local object library or direct text-to-pixel mappings, the proposed approach utilizes knowledge retrieved from Internet image search engines, which helps to generate diverse and creative scenes. The proposed approach allows interactive tuning of the synthesized scene via NL. This helps to generate more complex and semantically meaningful scenes, and to correct training errors or bias. The success of robot behavior control and programming relies on correct estimation of task implementation status, which is comprised of robotic status and environmental status. Besides vision information to estimate environmental status, tactile information is heavily used to estimate robotic status. In this dissertation, correlation based approaches have been developed to detect slippage occurrence and slipping velocity, which provide grasp status to the high symbolic level and are used to control grasp force at lower continuous level. The proposed approaches can be used with different sensor signal type and are not limited to customized designs. The proposed NL based robot control and programming approaches in this dissertation can be applied to other robotic applications, and help to pave the way for flexible and safe human-robot collaboration."--Pages ii-iii.
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Title: Low rank models for multi-dimensional data recovery and image super-resolution
Creator: Al-Qizwini, Mohammed
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "In the past decade tremendous research efforts focused on signals with specific features, especially sparse and low rank signals. Researchers showed that these signals can be recovered from much smaller number of samples than the Nyquist rate. These efforts were promising for several applications in which the nature of the data is known to be sparse or low rank, but the available samples are much fewer than what is required by the traditional signal processing algorithms to grant an exact...
Show more"In the past decade tremendous research efforts focused on signals with specific features, especially sparse and low rank signals. Researchers showed that these signals can be recovered from much smaller number of samples than the Nyquist rate. These efforts were promising for several applications in which the nature of the data is known to be sparse or low rank, but the available samples are much fewer than what is required by the traditional signal processing algorithms to grant an exact recovery. Our objective in the first part of this thesis is to develop new algorithms for low rank data recovery from few observed samples and for robust low rank and sparse data separation using the Robust Principal Component Analysis (RPCA). Most current approaches in this class of algorithms are based on using the computationally expensive Singular Value Decomposition (SVD) in each iteration to minimize the nuclear norm. In particular, we first develop new algorithms for low rank matrix completion that are more robust to noise and converge faster than the previous algorithms. Furthermore, we generalize our recovery function to the multi-dimensional tensor domain to target the applications that deal with multi-dimensional data. Based on this generalized function, we propose a new tensor completion algorithm to recover multi-dimensional tensors from few observed samples. We also used the same generalized functions for robust tensor recovery to reconstruct the sparse and low rank tensors from the tensor that is formed by the superposition of those parts. The experimental results for this application showed that our algorithms provide comparable performance, or even outperforms, state-of-the-art matrix completion, tensor completion and robust tensor recovery algorithms; but at the same time our algorithms converge faster. The main objective of the second part of the thesis develops new algorithms for example based single image super-resolution. In this type of applications, we observe a low-resolution image and using some external "example" high-resolution - low-resolution images pairs, we recover the underlying high-resolution image. The previous efforts in this field either assumed that there is a one-to-one mapping between low-resolution and high-resolution image patches or they assumed that the high-resolution patches span the lower dimensional space. In this thesis, we propose a new algorithm that parts away from these assumptions. Our algorithm uses a subspace similarity measure to find the closes high-resolution patch to each low-resolution patch. The experimental results showed that DMCSS achieves clear visual improvements and an average of 1dB improvement in PSNR over state-of-the-art algorithms in this field. Under this thesis, we are currently pursuing other low rank and image super-resolution applications to improve the performance of our current algorithms and to find other algorithms that can run faster and perform even better."--Pages ii-iii.
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Title: Instrument automation and measurement data curation platform for enhancing research reproducibility and knowledge discovery
Creator: Gtat, Yousef
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Many applications demand the continued development of sensing systems that employ smart sensors, instrumentation circuits, and signal processing techniques to extract relevant information from real-world environments. In the engineering efforts to develop new sensors, tasks such as instrument automation, measurement process curation, real-time data acquisition, data analysis, and long-term tracking of inter-related datasets generate a significant volume and variety of information that is...
Show more"Many applications demand the continued development of sensing systems that employ smart sensors, instrumentation circuits, and signal processing techniques to extract relevant information from real-world environments. In the engineering efforts to develop new sensors, tasks such as instrument automation, measurement process curation, real-time data acquisition, data analysis, and long-term tracking of inter-related datasets generate a significant volume and variety of information that is challenging to organize, record, and analyze. Sensor development and characterization experiments can be laborious, prone to human error, difficult to repeat precisely, and can produce data that are challenging to interpret. Such issues highlight a need for a structured, automated approach to curate measurement processes and data acquisition. This thesis presents the first software platform for i) digitally designing measurement recipes, ii) remotely scheduling and monitoring experiment execution, iii) automatic data acquisition, iv) analyzing and storing results datasets, and v) linking the datasets with their prospective meta-datasets for deeper analysis and inspection. The proposed platform is flexible and capable of managing a large set of diverse instruments, measurement recipes and sensor datasets. By employing several design abstractions, it allows users to remotely design, schedule, monitor and execute measurement-based experiments while archiving results along with their information-rich metadata therefore preserving the provenance of the datasets. The platform enable precise timing control of instruments and stimulus signals along with long-term tracking of datasets eliminating manual errors and human omissions thus enhancing research reproducibility and promoting knowledge discovery methodologies."--Page ii.
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Title: Applying evolutionary computation techniques to address environmental uncertainty in dynamically adaptive systems
Creator: Ramirez, Andres J.
Date: 2013
Collection: Electronic Theses & Dissertations
Description: A dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of...
Show moreA dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of system and environmental conditions that a DAS might encounter throughout its lifetime. Nevertheless, a DAS must continuously satisfy its requirements despite the threat that this uncertainty poses to its adaptation capabilities. This dissertation proposes a model-based framework that supports the specification, monitoring, and dynamic reconfiguration of a DAS to explicitly address uncertainty. The proposed framework uses goal-oriented requirements models and evolutionary computation techniques to derive and fine-tune utility functions for requirements monitoring in a DAS, identify combinations of system and environmental conditions that adversely affect the behavior of a DAS, and generate adaptations on-demand to transition the DAS to a target system configuration while preserving system consistency. We demonstrate the capabilities of our model-based framework by applying it to an industrial case study involving a remote data mirroring network that efficiently distributes data even as network links fail and messages are dropped, corrupted, and delayed.
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Title: RESILIENT AERIAL AUTONOMY THROUGH EXTENDED HIGH-GAIN OBSERVERS
Creator: Boss, Connor James
Date: 2021
Collection: Electronic Theses & Dissertations
Description: For a growing number of flight operations, human piloted aircraft are being replaced by au- tonomous uncrewed aerial vehicles (UAVs) which can provide equivalent service with drastically reduced operating costs. The UAVs that are deployed in mission critical applications, such as search and rescue, medical deliveries to remote locations, infrastructure inspection, and reconnaissance and surveillance, must be extremely resilient as the loss of a vehicle poses significant threats to financial,...
Show moreFor a growing number of flight operations, human piloted aircraft are being replaced by au- tonomous uncrewed aerial vehicles (UAVs) which can provide equivalent service with drastically reduced operating costs. The UAVs that are deployed in mission critical applications, such as search and rescue, medical deliveries to remote locations, infrastructure inspection, and reconnaissance and surveillance, must be extremely resilient as the loss of a vehicle poses significant threats to financial, security, or personnel interests. In order to rely on uncrewed systems in these mission critical situations, we must have confidence in their ability to perform their duties as reliably as possible. Recent advancements in hardware, software, and control design have increased the re- liability of these small inexpensive aircraft. The focus of this dissertation is to further improve multi-rotor reliability in the presence of a broad class of disturbances, while providing a unifying framework that can be extended to multiple applications.The methods we present in this work are based on a feedback linearizing control strategy in which the controller is augmented with an extended high-gain observer. The addition of the extended high-gain observer allows us to overcome the typical drawbacks associated with using a feedback linearization control approach; primarily that we must have an excellent model of the system, and we must know any disturbances that are affecting the system. The extended high-gain observer not only provides estimates of any model uncertainties or external disturbances, but also any unmeasured states for use in output feedback control. This estimation and control strategy enables the multi-rotor to robustly track a trajectory in the presence of a broad class of unmodeled disturbances and without needing an extremely accurate system model. This method forms the base technology applied throughout this dissertation. We extend our estimation and control strategy in a number of ways in the coming chapters. We begin by extending the observer dynamics further to incorporate real-time trajectory estimation for a reference system which may have partially known or completely unknown dynamics, and for which we assume we only have access to the position of the reference system. The observer is then able to provide estimates of all higher-order trajectory terms required for feedforward control, improving transient tracking performance. We further extend this strategy to enable both the detection and classification of a complete actuator failure of a multi-rotor during flight. The identification subsequently enables a control reconfiguration and a recovery from the loss of an actuator to resume flight operations. This extension provides the ability to not only detect and correctly identify a failure, but to discern the failure from other external disturbances affecting the system during flight. Finally, we extend our estimation and control strategy to enable robust trajectory tracking for a novel form of multi-body multi-rotor systems. This type of system consists of a large carrier UAV which suspends a small platform which is itself actuated by a pair of rotors. The platform is equipped with a manipulator which enables long reach aerial manipulation. One advantage of this configuration is the workspace will not be disturbed by downwash from the carrier UAV. Each of these methods are rigorously analyzed to guarantee closed-loop stability and provide insights into the trade-offs that arise during the design process for these control systems utilizing extended high-gain observers.
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Title: Online innovization : towards knowledge discovery and achieving faster convergence in multi-objective optimization
Creator: Gaur, Abhinav
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Ì0300nnovization'' is a task of learning common principles thatexist among some or all of the Pareto-optimal solutions in amulti-objective optimization problem. Except a few earlierstudies, most innovization related studies were performed onthe final non-dominated solutions found by an evolutionary multi-objective algorithm eithermanually or by using a machine learning method.Recent studies have shown that these principles can be learnedduring intermediate iterations of an optimization run...
Show moreÌ0300nnovization'' is a task of learning common principles thatexist among some or all of the Pareto-optimal solutions in amulti-objective optimization problem. Except a few earlierstudies, most innovization related studies were performed onthe final non-dominated solutions found by an evolutionary multi-objective algorithm eithermanually or by using a machine learning method.Recent studies have shown that these principles can be learnedduring intermediate iterations of an optimization run and simultaneously utilized in thesame optimization run to repair variables to achieve a fasterconvergence to the Pareto-optimal set. This is what we are calling as ò0300nline innovization'' as it is performed online during the run of an evolutionary multi-objective optimization algorithm. Special attention is paid to learning rules that are easier to interpret, such as short algebraic expressions, instead of complex decision trees or kernel based black box rules.We begin by showing how to learn fixed form rules that are encountered frequently in multi-objective optimization problems. We also show how can we learn free form rules, that are linear combination of non-linear terms, using a custom genetic programming algorithm. We show how can we use the concept of k0300nee' in PO set of solutions along with a custom dimensional penalty calculator to discard rules that may be overly complex, or inaccurate or just dimensionally incorrect. The results of rules learned using this custom genetic programming algorithm show that it is beneficial to let evolution learn the structure of rules while the constituent weights should be learned using some classical learning algorithm such as linear regression or linear support vector machines. When the rules are implicit functions of the problem variables, we use a computationally inexpensive way of repairing the variables by turning the problem of repairing the variable into a single variable golden section search.We show the proof of concept on test problems by learning fixed form rules among variables of the problem, which we then use during the same optimization run to repair variables. Different principleslearned during an optimization run can involve differentnumber of variables and/or variables that arecommon among a number of principles. Moreover, a preferenceorder for repairing variables may play an important role forproper convergence. Thus, when multiple principles exist, itis important to use a strategy that is most beneficial forrepairing evolving population of solutions.The above methods are applied to a mix of test problems and engineering design problems. The results are encouraging and strongly supportsthe use of innovization task in enhancing the convergence of an evolutionary multi-objective optimization algorithms. Moreover, the custom genetic program developed in this work can be a useful machine learning tool for practitioners to learn human interpretable rules in the form of algebraic expressions.
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Title: Design, fabrication, characterization, and control of VO2-based micro-electro-mechanical actuators
Creator: Merced, Emmanuell J.
Date: 2014
Collection: Electronic Theses & Dissertations
Description: In this work, a vanadium dioxide (VO2)-based micro-electro-mechanical actuator has been successfully designed, fabricated, characterized and controlled to achieve accurate displacements through the monolithic integration of a localized heater and self-sensing mechanism. VO2 is a solid-to-solid phase transition material whose electrical, structural, and optical properties change abruptly as a function of temperature. Recent integration of this material in micro-actuators has shown strain...
Show moreIn this work, a vanadium dioxide (VO2)-based micro-electro-mechanical actuator has been successfully designed, fabricated, characterized and controlled to achieve accurate displacements through the monolithic integration of a localized heater and self-sensing mechanism. VO2 is a solid-to-solid phase transition material whose electrical, structural, and optical properties change abruptly as a function of temperature. Recent integration of this material in micro-actuators has shown strain energy densities, displacements, actuation speeds, and repeatability values comparable or, in some cases, superior to state-of-the-art micro-actuator technologies. Previous studies on VO2 micro-actuators focus on open-loop manipulation of the device deflection, whose performance is highly susceptible to environmental disturbances and noises. In order to obtain accurate deflection control in micro-actuators, a closed-loop configuration is generally employed, which involves the use of external or internal displacement sensors. The incorporation of these sensors in micro-actuators usually increase design complexity, fabrication cost, and system footprint. Due to the multifunctional nature of VO2, a self-sensing technique is achieved, where the micro-actuator deflection is estimated through VO2 resistance measurements. In addition, the resistance-deflection hysteretic behavior is largely reduced due to the strong correlation between the electrical and structural transition, which greatly simplifies the self-sensing model. The closed-loop deflection control of these devices using self-heating actuation is also studied through voltage and current control, which reduces the need for additional heating components.
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Title: Robust control of systems with piecewise linear hysteresis
Creator: Edardar, Mohamed Mohamed
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Hysteresis nonlinearity is found in many control system applications such as piezo-actuated nanopositioners. The positioner is represented as a linear system preceded by hysteresis. This hysteresis nonlinearity is usually modeled by operators in order to simulate their effects in the closed-loop system or to use their inverse to compensate for their effects. In order to reduce the hysteresis effect, an approximate inverse operator is used as a feedforward compensator. The first part of our...
Show moreHysteresis nonlinearity is found in many control system applications such as piezo-actuated nanopositioners. The positioner is represented as a linear system preceded by hysteresis. This hysteresis nonlinearity is usually modeled by operators in order to simulate their effects in the closed-loop system or to use their inverse to compensate for their effects. In order to reduce the hysteresis effect, an approximate inverse operator is used as a feedforward compensator. The first part of our work considers driving an upper bound on the inversion error using the hysteresis model. This bound is a function of the input references, which is much less conservative than constant bounds. It is used in designing the closed-loop control systems. The second part is to design feedback controller to achieve the desired performance. Three different methods are used throughout this work and a comparison between them is also provided. First, we use the conventional proportional Integral (PI) control method, which is extensively used in commercial applications. However, in our method we add a feedforward component which improves the performance appreciably. Second, a sliding-mode-control (SMC) scheme is used because it is one of the very powerful nonlinear robust control methods. Other schemes like high gain feedback and Lyapunov redesign have close results to SMC and hence it is not included in this work. The third control is H∞ control. It is a robust linear control method, which deals with uncertainty in the system in an optimal control structure. Unlike the PI controller, the H∞ controller uses the features of the linear plant in the design which allows to accomplish more than the simple PI controller. Mainly, it can shape the closed-loop transfer function of the system to achieve the design objectives. Including the operators in the closed-loop system, makes it hard to obtain explicit solutions of the dynamics using conventional methods. We exploit two features of piezoelectric actuators to provide a complete solution of the tracking error. First, the hysteresis is approximated by a piece-wise linear operator. Second, the linear plant has a large bandwidth which allows using singular perturbation techniques to put the system in a time-scale structure. We show that the slope of a hysteresis loop segment plays an important role in determining the error size. Our analysis also shows how error is affected by increasing the frequency of the reference input. We verify that the accumulation of the error, which is propagating from segment to another is bounded and derive its limit. We provide a comparison between simulation and the analytic expressions of the tracking error at different frequencies. Experimental results are also presented to show the effectiveness of our controllers compared with other techniques.
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Title: Layout optimization of truss structures by fully stressed design evolution strategy
Creator: Ahrari, Ali
Date: 2016
Collection: Electronic Theses & Dissertations
Description: "The field of structural optimization has gained much academic interest in the recent decades. Different streams of optimization methods have been applied to this problem including analytical methods, optimality criteria-based method and gradient-based methods. During the recent decade, there has been a growing interest among researchers to apply stochastic population-based methods, the so-called meta-heuristics, to this class of optimization problems. The motivation is the robustness and...
Show more"The field of structural optimization has gained much academic interest in the recent decades. Different streams of optimization methods have been applied to this problem including analytical methods, optimality criteria-based method and gradient-based methods. During the recent decade, there has been a growing interest among researchers to apply stochastic population-based methods, the so-called meta-heuristics, to this class of optimization problems. The motivation is the robustness and capability of meta-heuristics to avoid local minima. On the downside, their required evaluation budget grows fast when the number of design variables is increased, which limits the complexity of problems to which they can be applied. Furthermore, majority of these methods are tailored to optimize only the cross-sectional areas of the members, the potential saving from which is highly limited. At the same time, several factors have diminished practitioners' interests in the academic research on this topic, including simplicity of conventional test problems compared to real structures, variety of design constraints in practice and the complexity of evaluation of the total cost. This dissertation aims at addressing some of the most critical shortcomings in the available truss optimization methods, both from academic and practical perspectives. It proposes a novel bi-level method for simultaneous optimization of topology, shape and size of truss structures. In the upper level, a specialized evolution strategy (ES) is proposed which follows the principles of contemporary evolution strategies (ESs), although the formulation is modified to handle mixed- variable highly constrained truss optimization problems. The concept of fully stressed design is employed in the lower level as an efficient method for resizing the sampled solution in the upper level. The concept of fully stressed design is also utilized to define a specialized penalty term based on the estimated required increase in the structural weight such that all constraints are satisfied. The proposed method, called fully stressed design evolution strategy (FSD-ES), is developed in four stages. It is tested on complicated problems, some of which are developed in this dissertation, as an attempt to reduce the gap between complexity of test problems and real structures. Empirical evaluation and comparison with the best available methods in the literature reveal superiority of FSD-ES, which intensifies for more complicated problems. Aside from academically interesting features of FSD-ES, it addresses some of the practicing engineers' critiques on applicability of truss optimization methods. FSD-ES can handle large-scale truss optimization problems with more than a thousand design parameters, in a reasonable amount of CPU time. Our numerical results demonstrate that the optimized design can hardly be guessed by engineering intuition, which demonstrates superiority of such design optimization methods. Besides, the amount of material saving is potentially huge, especially for more complicated problems, which justifies simulation cost of the design problem. FSD-ES does not require any user-dependent parameter tuning and the code is ready to use for an arbitrary truss design problem within the domain of the code."--Pages ii-iii.
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Title: COLLABORATIVE DISTRIBUTED DEEP LEARNING SYSTEMS ON THE EDGES
Creator: Zeng, Xiao
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Deep learning has revolutionized a wide range of fields. In spite of its success, most deep learning systems are proposed in the cloud, where data are processed in a centralized manner with abundant compute and network resources. This raises a problem when deep learning is deployed on the edge where distributed compute resources are limited. In this dissertation, we propose three distributed systems to enable collaborative deep learning on the edge. These three systems target different...
Show moreDeep learning has revolutionized a wide range of fields. In spite of its success, most deep learning systems are proposed in the cloud, where data are processed in a centralized manner with abundant compute and network resources. This raises a problem when deep learning is deployed on the edge where distributed compute resources are limited. In this dissertation, we propose three distributed systems to enable collaborative deep learning on the edge. These three systems target different scenarios and tasks. The first system dubbed Distream is a distributed live video analytics system based on the smart camera-edge cluster architecture. Distream fully utilizes the compute resources at both ends to achieve optimized system performance. The second system dubbed Mercury is a system that addresses the key bottleneck of collaborative learning. Mercury enhances the training efficiency of on-device collaborative learning without compromising the accuracies of the trained models. The third system dubbed FedAce is a distributed training system that improves training efficiency under federated learning setting where private on-device data are not allowed to be shared among local devices. Within each participating client, FedAce achieves such improvement by prioritizing important data. In the server where model aggregation is performed, FedAce exploits the client importance and prioritizes important clients to reduce stragglers and reduce the total number of rounds. In addition, FedAce conducts federated model compression to reduce the per-round communication cost and obtains a compact model after training completes. Extensive experiments show that the proposed three systems are able to achieve significant improvements over status-quo systems.
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Title: Observers as a tool to reduce information exchange and increase convergence rate in multi-agent systems
Creator: Chowdhury, Dhrubajit
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Observers form an integral part of output feedback control of linear and nonlinear systems. This dissertation investigates the use of observers in multi-agent systems to reduce information exchange and increase the convergence rate. Multi-agent systems have been immensely popular since the last two decades due to their broad applicability in practical problems, some of them being distributed sensor networks, formation control, and cooperative robotics. The controller for each agent is...
Show moreObservers form an integral part of output feedback control of linear and nonlinear systems. This dissertation investigates the use of observers in multi-agent systems to reduce information exchange and increase the convergence rate. Multi-agent systems have been immensely popular since the last two decades due to their broad applicability in practical problems, some of them being distributed sensor networks, formation control, and cooperative robotics. The controller for each agent is distributed in nature, which means that it only depends on the local information available to it. The distributed approach has several advantages such as less computational effort, reliability, etc., compared to the centralized one where there is a central agent that does all the computations and then makes the decision.The convergence rate of consensus algorithms is an important performance measure. We show that by using observers, we can increase the convergence rate of the consensus algorithm. The observer is used for estimating the missing links at each agent. We also study the effect of increasing network size on the consensus algorithm. For networks without a leader, the rate of convergence of the consensus protocol becomes slow for certain classes of graphs, while for networks with a single leader, the convergence rate becomes slow for undirected graphs. We design scalable consensus algorithms for first-order linear agents and second-order nonlinear heterogeneous agents where the convergence rate remains almost invariant of the network size.We consider the case of reduced information exchange in a network of nonlinear heterogeneous agents having the same relative degree r. We use observers along with feedback control to compensate for the heterogeneity at each agent. Finally, motivated by the practical application of multi-agent systems to power systems frequency synchronization, we fuse dynamic consensus algorithms with observers to achieve practical frequency synchronization under time-varying power-demand. We show that the frequency synchronization error can be made arbitrarily small by tuning controller and observer parameters.
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Title: Adaptive on-device deep learning systems
Creator: Fang, Biyi
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Mobile systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. On-device deep learning is regarded as the key enabling technology for realizing their full potential. This is because communication with cloud adds additional latency or cost, or the applications must operate even with intermittent internet connectivity.The key to achieving the full promise of these mobile vision systems is effectively analyzing the streaming video frames. However,...
Show more"Mobile systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. On-device deep learning is regarded as the key enabling technology for realizing their full potential. This is because communication with cloud adds additional latency or cost, or the applications must operate even with intermittent internet connectivity.The key to achieving the full promise of these mobile vision systems is effectively analyzing the streaming video frames. However, processing streaming video frames taken in mobile settings is challenging in two folds. First, the processing usually involves multiple computer vision tasks. This multi-tenant characteristic requires mobile vision systems to concurrently run multiple applications that target different vision tasks. Second, the context in mobile settings can be frequently changed. This requires mobile vision systems to be able to switch applications to execute new vision tasks encountered in the new context.In this article, we fill this critical gap by proposing NestDNN, a framework that enables resource-aware multi-tenant on-device deep learning for continuous mobile vision. NestDNN enables each deep learning model to offer flexible resource-accuracy trade-offs. At runtime,it dynamically selects the optimal resource-accuracy trade-off for each deep learning model to fit the model's resource demand to the system's available runtime resources. In doing so, NestDNN efficiently utilizes the limited resources in mobile vision systems to jointly maximize the performance of all the concurrently running applications.Although NestDNN is able to efficiently utilize the resource by being resource-aware, it essentially treats the content of each input image equally and hence does not realize the full potential of such pipelines. To realize its full potential, we further propose FlexDNN, a novel content-adaptive framework that enables computation-efficient DNN-based on-device video stream analytics based on early exit mechanism. Compared to state-of-the-art earlyexit-based solutions, FlexDNN addresses their key limitations and pushes the state-of-the-artforward through its innovative fine-grained design and automatic approach for generating the optimal network architecture."--Pages ii-iii.
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Title: Integrated modeling and control of flexible aircraft wings
Creator: Wehr, Dagmara Anna
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Structural control for vibration reduction has important applications in many research areas, including the effect of earthquakes on buildings and aerodynamic forces on aircraft stability and performance. Both passive and active control techniques have been implemented, with the best solution usually involving a passive approach followed by an active one. This thesis presents an integrated modeling and controller design approach. Modal Cost Analysis (MCA) and Output Covariance Constraint (OCC...
Show moreStructural control for vibration reduction has important applications in many research areas, including the effect of earthquakes on buildings and aerodynamic forces on aircraft stability and performance. Both passive and active control techniques have been implemented, with the best solution usually involving a passive approach followed by an active one. This thesis presents an integrated modeling and controller design approach. Modal Cost Analysis (MCA) and Output Covariance Constraint (OCC) control are used to reduce a high-order aeroelastic wing model to establish the best controller for the reduced-order model, with a constraint on the covariance of the vibration outputs. MCA seeks to keep the modes that have the highest contribution to a given cost function. Using iterations on the two processes will allow a lower-order controller to be designed and result in the same performance.The OCC and MCA methods and their respective algorithms are presented, and an approach to integrate the two procedures is given. NASA's model used in this thesis is applied to the MCA and OCC algorithms using MATLAB. A 40th-order wing model is derived. The model reduction technique initially reduces the system to a 12th order one. A simulation of the OCC algorithm is performed on the reduced-order model and applied to the full-order model. The controller resulting in the best closed-loop performance is shown to significantly reduce the vibrations due to wind. A corresponding weighting matrix used in OCC is then used for a second round of MCA to further reduce the model to an 8th order model. A lower-order controller designed for this second model is shown to similarly reduce the output vibrations.
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Title: Optimal Learning of Deployment and Search Strategies for Robotic Teams
Creator: Wei, Lai
Date: 2021
Collection: Electronic Theses & Dissertations
Description: In the problem of optimal learning, the dilemma of exploration and exploitation stems from the fact that gathering information and exploiting it are, in many cases, two mutually exclusive activities. The key to optimal learning is to strike a balance between exploration and exploitation. The Multi-Armed Bandit (MAB) problem is a prototypical example of such an explore-exploit tradeoff, in which a decision-maker sequentially allocates a single resource by repeatedly choosing one among a set of...
Show moreIn the problem of optimal learning, the dilemma of exploration and exploitation stems from the fact that gathering information and exploiting it are, in many cases, two mutually exclusive activities. The key to optimal learning is to strike a balance between exploration and exploitation. The Multi-Armed Bandit (MAB) problem is a prototypical example of such an explore-exploit tradeoff, in which a decision-maker sequentially allocates a single resource by repeatedly choosing one among a set of options that provide stochastic rewards. The MAB setup has been applied in many robotics problems such as foraging, surveillance, and target search, wherein the task of robots can be modeled as collecting stochastic rewards. The theoretical work of this dissertation is based on the MAB setup and three problem variations, namely heavy-tailed bandits, nonstationary bandits, and multi-player bandits, are studied. The first two variations capture two key features of stochastic feedback in complex and uncertain environments: heavy-tailed distributions and nonstationarity; while the last one addresses the problem of achieving coordination in uncertain environments. We design several algorithms that are robust to heavy-tailed distributions and nonstationary environments. Besides, two distributed policies that require no communication among agents are designed for the multi-player stochastic bandits in a piece-wise stationary environment.The MAB problems provide a natural framework to study robotic search problems. The above variations of the MAB problems directly map to robotic search tasks in which a robot team searches for a target from a fixed set of view-points (arms). We further focus on the class of search problems involving the search of an unknown number of targets in a large or continuous space. We view the multi-target search problem as a hot-spots identification problem in which, instead of the global maximum of the field, all locations with a value greater than a threshold need to be identified. We consider a robot moving in 3D space with a downward-facing camera sensor. We model the robot's sensing output using a multi-fidelity Gaussian Process (GP) that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm that (i) addresses the coverage-accuracy tradeoff: sampling at a location farther from the floor provides a wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the detection time.An approach to extend the single robot search policy to multiple robots is to partition the environment into multiple regions such that workload is equitably distributed among all regions and then assign a robot to each region. The coverage control focuses on such equitable partitioning and the workload is equivalent to the so-called service demands in the coverage control literature. In particular, we study the adaptive coverage control problem, in which the demands of robotic service within the environment are modeled as a GP. To optimize the coverage of service demands in the environment, the team of robots aims to partition the environment and achieve a configuration that minimizes the coverage cost, which is a measure of the average distance of a service demand from the nearest robot. The robots need to address the explore-exploit tradeoff: to minimize coverage cost, they need to gather information about demands within the environment, whereas information gathering deviates them from maintaining a good coverage configuration. We propose an algorithm that schedules learning and coverage epochs such that its emphasis gradually shifts from exploration to exploitation while never fully ceasing to learn. Using a novel definition of coverage regret, we analyze the algorithm and characterizes its coverage performance over a finite time horizon.
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Title: Enhancing high-gain-observer performance in the presence of measurement noise
Creator: Ball, Alexis A.
Date: 2011
Collection: Electronic Theses & Dissertations
Description: High-gain observers are a prevalent and an important topic in state estimation and output feedback control of nonlinear systems. In the absence of measurement noise, this technique robustly estimates the derivatives of the output while achieving fast convergence. Moreover, for a sufficiently fast observer and a globally bounded controller, the high-gain observer is able to recover the system performance achieved under state feedback control.However, in the presence of measurement noise, a...
Show moreHigh-gain observers are a prevalent and an important topic in state estimation and output feedback control of nonlinear systems. In the absence of measurement noise, this technique robustly estimates the derivatives of the output while achieving fast convergence. Moreover, for a sufficiently fast observer and a globally bounded controller, the high-gain observer is able to recover the system performance achieved under state feedback control.However, in the presence of measurement noise, a tradeoff exists between the measurement noise sensitivity and the speed of state reconstruction. As the observer gain is increased, the bandwidth of the observer is extended. As the bandwidth increases, the high-gain observer asymptotically approaches the behavior of a differentiator,exacerbating the presence of measurement noise.This dissertation addresses the challenging performance issues that arise when implementing high-gain observers in the presence of measurement noise. In particular, we focus on the tradeoff between fast state reconstruction, minimizing the bound on the steady-state estimation error, and rejecting the model uncertainty. The observerdesign and analysis is approached through three major thrust areas: observer structure, tracking performance and filtering.
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Title: EPIDEMIC MODELS UNDER MOBILITY ON MULTI-LAYER NETWORKS
Creator: Abhishek, Vishal
Date: 2021
Collection: Electronic Theses & Dissertations
Description: We study epidemic spreading models namely, SIS and SIR models, under mobility on multilayer networks. In particular, we consider a patchy environment in which each patch comprises individuals belonging the different classes, e.g., individuals in different socio-economic strata. We model the mobility of individuals of each class across different patches through an associated Continuous Time Markov Chain (CTMC). The topology of these multiple CTMCs constitute the multi-layer network of mobility...
Show moreWe study epidemic spreading models namely, SIS and SIR models, under mobility on multilayer networks. In particular, we consider a patchy environment in which each patch comprises individuals belonging the different classes, e.g., individuals in different socio-economic strata. We model the mobility of individuals of each class across different patches through an associated Continuous Time Markov Chain (CTMC). The topology of these multiple CTMCs constitute the multi-layer network of mobility. At each time, individuals in the multi-layer network of spatially-distributed patches move according to their CTMC and subsequently interact with the local individuals in the patch according to SIS or SIR models. We establish the existence of various equilibria under different parameter regimes and establish their (almost) global asymptotic stability using Lyapunov techniques. We also derive simple conditions that highlight the influence of the multi layer network on the stability of these equilibria. We numerically illustrate that the derived model provides a good approximation to the stochastic model with a finite population and also demonstrate the influence of the multi-layer network structure.Next, we extend some of the results to the case of weakly connected networks. Here, we use the notion of strongly connected components and input to state stability to study the stability of equilibria. Finally, we consider a resource allocation problem to maximize the rate of convergence to an equilibrium. We show that under certain assumptions the problem can be formulated as a geometric program. We provide numerical illustrations to corroborate the results.
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Title: A STUDY ON FLUID-STRUCTURE INTERACTION OF SWIMMING BEAM USING IMMERSED BOUNDARY- LATTICE BOLTZMANN METHOD
Creator: Rahman, Md Towhidur
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Fluid-structure interaction (FSI) study is of great importance to understand the hydrodynamic coupling of biological swimmers in surrounding environmental domain. Multiple numerical and experimental studies have taken place to capture the behavioral pattern from the environment, explore the physical phenomena and comprehension of dynamics to make contribution in real life applications. In this study, an immersed boundary-lattice Boltzmann method (IB-LBM) for fluid-structure interaction...
Show moreFluid-structure interaction (FSI) study is of great importance to understand the hydrodynamic coupling of biological swimmers in surrounding environmental domain. Multiple numerical and experimental studies have taken place to capture the behavioral pattern from the environment, explore the physical phenomena and comprehension of dynamics to make contribution in real life applications. In this study, an immersed boundary-lattice Boltzmann method (IB-LBM) for fluid-structure interaction problems is presented. The impact of solid structure on to the surrounding fluid domain is dealt with by immersed boundary method (IBM), where the structure is assumed to be immersed into surrounding fluid and the effect of the immersed boundary are considered by exertion of Lagrangian force onto the surrounding fluid grid points as body force. The flow dynamics is determined by solving discrete lattice Boltzmann equation of a single relaxation time model. The structural dynamics are solved by the finite difference method. For solving the structural dynamics, inextensibility condition was applied. A staggered grid is used in the Lagrangian coordinate system, where tension force is defined on the interfaces (half-grids) and other variables are defined on the nodes. Tension force is calculated at the intermediate steps and used as inextensibility constraint to obtain filament position at the next time step. In the present study, a detailed derivation and corresponding discretization is done for multiple free-swimming cases for a thin flexible filament. The thin flexible filament is actuated by imposing oscillatory heaving and pitching motion at the leading edge with prescribed control parameters. The flow physics of the system is investigated and pressure on the surfaces of the flexible filament is obtained. The results obtained in this study shows consistency with previous publications. The presented computational modelling may be used in future with multiple obstacles in the domain, to investigate the surface pressure variation of the swimming flexible filament and generated data sets may contribute to optimization of control mechanism of the swimmer.
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Title: Real-time multimodal sensing in nano/bio environment
Creator: Song, Bo
Date: 2016
Collection: Electronic Theses & Dissertations
Description: As a sensing device in nano-scale, scanning probe microscopy (SPM) is a powerful tool for exploring nano world. Nevertheless two fundamental problems tackle the development and application of SPM based imaging and measurement: slow imaging/measurement speed and inaccuracy of motion or position control. Usually, SPM imaging/properties measuring speed is too slow to capture a dynamic observation on sample surface. In addition, Both SPM imaging and properties measurement always experience...
Show moreAs a sensing device in nano-scale, scanning probe microscopy (SPM) is a powerful tool for exploring nano world. Nevertheless two fundamental problems tackle the development and application of SPM based imaging and measurement: slow imaging/measurement speed and inaccuracy of motion or position control. Usually, SPM imaging/properties measuring speed is too slow to capture a dynamic observation on sample surface. In addition, Both SPM imaging and properties measurement always experience positioning inaccuracy problems caused by hysteresis and creep of the piezo scanner. This dissertation will try to solve these issues and proposed a SPM based real-time multimodal sensing system which can be used in nano/bio environment. First, a compressive sensing based video rate fast SPM imaging system is shown as an efficient method to dynamically capture the sample surface change with the imaging speed 1.5 frame/s with the scan size of 500 nm * 500 nm. Besides topography imaging, a new additional modal of SPM: vibration mode, will be introduced, and it is developed by us to investigate the subsurface mechanical properties of the elastic sample such as cells and bacteria. A followed up study of enzymatic hydrolysis will demonstrate the ability of in situ observation of single molecule event using video rate SPM. After that we will introduce another modal of this SPM sensing system: accurate electrical properties measurement. In this electrical properties measurement mode, a compressive feedbacks based non-vector space control approach is proposed in order to improve the accuracy of SPM based nanomanipulations. Instead of sensors, the local images are used as both the input and feedback of a non-vector space closed-loop controller. A followed up study will also be introduced to shown the important role of non-vector space control in the study of conductivity distribution of multi-wall carbon nanotubes. At the end of this dissertation, some future work will be also proposed to fulfill the development and validation of this real-time multimodal sensing system.
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Title: Development of a position sensitive device and multi-position alignment control system for automated industrial robot calibration
Creator: Nieves-Rivera, Erick
Date: 2013
Collection: Electronic Theses & Dissertations
Description: This dissertation proposes a novel calibration system capable of automatically calibrating industrial robots. Often, inaccurate assumptions about real links parameters or even small offsets exist in the individual robot joints that lead to errors in the internal kinematic model equation and, as a consequence, affect the accuracy of a robotic system. To solve this problem, the proposed approach introduces a completely new technique for industrial robot calibration. The proposed system consists...
Show moreThis dissertation proposes a novel calibration system capable of automatically calibrating industrial robots. Often, inaccurate assumptions about real links parameters or even small offsets exist in the individual robot joints that lead to errors in the internal kinematic model equation and, as a consequence, affect the accuracy of a robotic system. To solve this problem, the proposed approach introduces a completely new technique for industrial robot calibration. The proposed system consists of an industrial robot manipulator, a camera, a laser fixture attached to the robot tool center point (TCP), a PC-based interface, and a new position sensitive calibration device (PSCD). This wireless calibration device is comprised of two fixed position sensitive detectors (PSDs) tilted with an angle between them to reflect the laser line from one PSD to the other. Such a device is capable of feeding back the movement information needed to localize the TCP frame relative to the device frame. The new calibration approach is not only able to compute the joint offset parameters of the robot but is also capable of simultaneously calibrating the robot's workpiece relationship. It was also designed to be faster, simpler and cheaper than any other methods. Throughout this dissertation, the newly developed calibration device, the principle of our calibration system and the control approach needed to achieve automation of the entire system are presented and discussed. Finally, the feasibility of the overall calibration system including device hardware, software and calibration algorithms was demonstrated with experimental results.
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Title: Development of large scale structured light based measurement systems
Creator: Zhang, Chi
Date: 2012
Collection: Electronic Theses & Dissertations
Description: The development of large scale structured light based dimensional measurement systems is introduced in this study. Based on different applications, there are generally two different research directions for a structured light system: accuracy 3D inspection and fast environmental reconstruction. The first one is emphasized on the accuracy of the results and the second one is mainly concerned about reconstruction speed. Both of them become challenge tasks when the systems' scales increase...
Show moreThe development of large scale structured light based dimensional measurement systems is introduced in this study. Based on different applications, there are generally two different research directions for a structured light system: accuracy 3D inspection and fast environmental reconstruction. The first one is emphasized on the accuracy of the results and the second one is mainly concerned about reconstruction speed. Both of them become challenge tasks when the systems' scales increase.Quality inspection is a process to evaluate the quality of a manufactured work piece's 3D shape. Compared with traditional Coordinate Measurement Machine (CMM), structured light based optic measurement has the merits of fast speed, high inspection sample rate, and overall simplicity of operation, and becomes a popular alternative inspection method for CMM. The measurement result, point cloud of the test work piece, is compared with the Computer Aided Design (CAD) model to find the error distribution map. The color-code error distribution map will be further evaluated with the acceptable tolerance. There are basically three main issues, when the system scale is enlarged.Calibration of such a large system with long standoff distance, large Field Of View (FOV), deep Depth Of View (DOV), and multi-sensor became a challenge task. The calibration errors are enlarged when a large scale system is treated. In order to maintain high accuracy, an innovative 3D sensor model with less calibration parameters was developed. Instead of employing the incident light in projector frame, 3D point recovery was achieved within the camera frame via plane induced parallax information, in which projector's intrinsic and orientation are avoided in the 3D model. Precision of the large scale optic system was also simulated and was tested against the random image noise in the system. Multiply-plane strategy was developed and implemented to calibrate the sensor.As the system scale increased, more work pieces could be inspected at the same time. The optical properties became more complicated. Same with all the other vision based measurement system, structured light systems is usually weak against surface optical properties. Material exhibiting different color textures, reflection ratios, and especially a mixture between specula reflection and diffuse reflection, fails the optic sensor to correctly acquire incident light information. Traditional structure light method is only valid for parts with diffuse reflection property. Therefore pre-treatment of the surface have to be added before inspection. Aiming on this problem, a structured sensor with a robust surface decoding method for industry application was developed. The coding strategies were properly designed against variously test surface optic properties. Monochromatic light was utilized against different object color textures. The illumination of the projector was adjusted pixel by pixel based on the optic properties of the test material to composite different reflection coefficients and internal reflection. Furthermore, an extrapolation model to solve the internal reflection problem and a sub-pixel interpolation model to increase measurement accuracy were proposed too. The proposed system was capable to inspect various materials with different shapes and different optic properties, from black, dark, to shiny.Registration among sensor frame to the common was achieved based on Iterative Closest Point (ICP) method. The final point cloud joined by each individual point cloud could represent the 3D shape of a large work piece. Object-oriented on-line calibration was developed based on ICP and Geometry Dimension and Tolerance (GDT) information to register the final point cloud and the CAD model. Several modifications to traditional ICP are applied to speed up the registration process due to the large amount of points in both sets.Environmental reconstruction for navigation is a process to quickly acquire surrounding information around the vision sensor and presents a 3D fusion display for the operator. Traditional navigation system only employed a camera to view the environment, in which the depth information is lost. Operator is often confused with the object distance, the distance between the object and the camera. Structured light system based on infrared light could quickly rebuild objects depthes and fuse it into the displayed images without influence the operator's normal vision. The sensed points on the objects are highlighted by the color-codes: from red to blue, which are used to indicate the object distances.Fast environmental reconstruction emphasizes the acquisition time. In order against the moving objects, an one-shot surface coding algorithm was developed. Only one projection image is needed to acquire the 3D information. The codeword is determined in a single pattern because the code of each primitive depends on the values of the primitive and its neighbors. Compared with the previous patterns, this pattern is more robust because it can avoid the influence of the ambient light and the inspected part reflective property. Moreover, the requirement of the accuracy performance is achieved by the pattern primitive which is similar to the corner of the checker board since it can provide high accuracy performance even when the occlusion occurs.In order to reconstruct the environment without blind areas, an omni-direction panoramic structured light sensing system was developed to increase the system field of reconstruction. Hyperbolic mirrors are put in front of a projector and a camera. 3D reconstruction model was build up associated with the hyperbolic mirror. Task level calibration is conduct for the system. At last, a 360 image fused with depth information is achieved by the designed system.In summary, the study developed large scale structured light systems based on two different applications: accurate inspection for industry quality control and fast environmental reconstruction for mobile robot navigation.
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