Matching Items (119)
Description
Soft continuum robots with the ability to bend, twist, elongate, and shorten, similar to octopus arms, have many potential applications, such as dexterous manipulation and navigation through unstructured, dynamic environments. Novel soft materials such as smart hydrogels, which change volume and other properties in response to stimuli such as temperature, pH, and chemicals, can potentially be used to construct soft robots that achieve self-regulated adaptive reconfiguration through on-demand dynamic control of local properties. However, the design of controllers for soft continuum robots is challenging due to their high-dimensional configuration space and the complexity of modeling soft actuator dynamics. To address these challenges, this dissertation presents two different model-based control approaches for robots with distributed soft actuators and sensors and validates the approaches in simulations and physical experiments. It is demonstrated that by choosing an appropriate dynamical model and designing a decentralized controller based on this model, such robots can be controlled to achieve diverse types of complex configurations. The first approach consists of approximating the dynamics of the system, including its actuators, as a linear state-space model in order to apply optimal robust control techniques such as H∞ state-feedback and H∞ output-feedback methods. These techniques are designed to utilize the decentralized control structure of the robot and its distributed sensing and actuation to achieve vibration control and trajectory tracking. The approach is validated in simulation on an Euler-Bernoulli dynamic model of a hydrogel based cantilevered robotic arm and in experiments with a hydrogel-actuated miniature 2-DOF manipulator. The second approach is developed for soft continuum robots with dynamics that can be modeled using Cosserat rod theory. An inverse dynamics control approach is implemented on the Cosserat model of the robot for tracking configurations that include bending, torsion, shear, and extension deformations. The decentralized controller structure facilitates its implementation on robot arms composed of independently-controllable segments that have local sensing and actuation. This approach is validated on simulated 3D robot arms and on an actual silicone robot arm with distributed pneumatic actuation, for which the inverse dynamics problem is solved in simulation and the computed control outputs are applied to the robot in real-time.
ContributorsDoroudchi, Azadeh (Author) / Berman, Spring (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Si, Jennie (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2022
Description本文对中国制药企业并购溢价影响因素进行了研究，提出了对制药企业并购非常重要的两个新的影响因素：可生产药品批文和在研新药批文。本文以2011年1月—2019年12月间我国制药行业上市公司并购事件为样本，对在研新药和可生产药品批文的价值从四个维度度量：是否有在研新药和可生产药品批文；在研新药数量及可生产药品批文数量；根据创新药和仿制药两个类别进行细分；标的企业所拥有的在研新药和可生产药品批文的市场价值。论文发现药品批文对企业并购溢价的影响不是很显著。进一步的，本文探究了药品批文对主并企业的对被并购公司的估值的影响。实证结果表明，我国制药企业在并购估值时确实会考虑到在研新药和可生产药品批文的价值。本文还发现对于可生产药品来说，相对创新药，被并购公司持有的仿制药批文影响更显著。而对于在研新药来说，主并企业更看重在研的创新药，在研仿制药对并购估值的影响不大。最后，本文选取了两个代表性案例进一步分析和探讨药品批文对企业并购的影响。
ContributorsYe, Tao (Author) / Shen, Wei (Thesis advisor) / Chang, Chun (Thesis advisor) / Jiang, Zhan (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2022
Description汽车行业属于国家支柱型产业，创造了高额的产值，增加了就业岗位。随着汽车生产行业竞争日趋激烈的趋势影响，汽车经销商在未来会出现明显的分化，并且逐步向头部集中。基于这样的行业背景，本项研究开展汽车经销商整体经营和盈利能力等方面的详细深入分析，即系统整合汽车经销商业务运营层面和财务层面数据，结合统计研究方法，对经销商盈利能力进行系统且详实归因分析，从而试别驱动盈利能力的关键业务要素。其研究成果能够完善对行业发展规律和经营模式系统性理解，从而进一步指导该领域的相关业务实践，提高经销商整体经营业绩。本课题通过四个阶段来开展经销商整体经营与盈利归因的相关研究。首先，本课题梳理了中国汽车消费行业发展的历史，同时阐述样本期内（2018-2020年）国内宏观经济和汽车消费市场的特征进行，并介绍X品牌汽车经销商的地理分布、资质和业绩评级体系、自身经营特征以及汽车生产商对经销商扶持政策等方面。在第二阶段，本课题聚焦研究假设、模型与方法，通过对X品牌汽车经销商的业务结构和运营管理开展分析，并逐步识别影响经销商盈利的关键指标变量，并提出研究假设和相关模型（即时间序列模型和面板回归模型）。在第三阶段，本课题首先开展经销商相关信息整体性统计分析，获得关键业务指标在样本期内动态特征，并结合时间序列回归模型探讨各项业务指标对经销商整体盈利能力的影响程度。在第四阶段，本课题采用（个体）固定效应的面板回归模型来研究不同组别（控制）条件下经销商盈利能力的影响因素以及其盈利能力对这些因素的敏感程度，从而更深入和全面地揭示影响经销商盈利能力的潜在因素。
基于上述四阶段的研究结果，本研究进一步就提升经销商盈利能力展开讨论，并提出相应对策。本课题相关结论仅从X品牌汽车经销商经营和财务数据进行定性和定量分析获得，但衷心希望本研究的成果能够对汽车经销商改善经营业务方面能起到实践上的借鉴和指导意义。
ContributorsPan, Guangxiong (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhu, Qigui (Committee member) / Arizona State University (Publisher)
Created2022
Description
Multi-segment manipulators and mobile robot collectives are examples of multi-agent robotic systems, in which each segment or robot can be considered an agent. Fundamental motion control problems for such systems include the stabilization of one or more agents to target configurations or trajectories while preventing inter-agent collisions, agent collisions with obstacles, and deadlocks. Despite extensive research on these control problems, there are still challenges in designing controllers that (1) are scalable with the number of agents; (2) have theoretical guarantees on collision-free agent navigation; and (3) can be used when the states of the agents and the environment are only partially observable. Existing centralized and distributed control architectures have limited scalability due to their computational complexity and communication requirements, while decentralized control architectures are often effective only under impractical assumptions that do not hold in real-world implementations. The main objective of this dissertation is to develop and evaluate decentralized approaches for multi-agent motion control that enable agents to use their onboard sensors and computational resources to decide how to move through their environment, with limited or absent inter-agent communication and external supervision. Specifically, control approaches are designed for multi-segment manipulators and mobile robot collectives to achieve position and pose (position and orientation) stabilization, trajectory tracking, and collision and deadlock avoidance. These control approaches are validated in both simulations and physical experiments to show that they can be implemented in real-time while remaining computationally tractable. First, kinematic controllers are proposed for position stabilization and trajectory tracking control of two- or three-dimensional hyper-redundant multi-segment manipulators. Next, robust and gradient-based feedback controllers are presented for individual holonomic and nonholonomic mobile robots that achieve position stabilization, trajectory tracking control, and obstacle avoidance. Then, nonlinear Model Predictive Control methods are developed for collision-free, deadlock-free pose stabilization and trajectory tracking control of multiple nonholonomic mobile robots in known and unknown environments with obstacles, both static and dynamic. Finally, a feedforward proportional-derivative controller is defined for collision-free velocity tracking of a moving ground target by multiple unmanned aerial vehicles.
ContributorsSalimi Lafmejani, Amir (Author) / Berman, Spring (Thesis advisor) / Tsakalis, Konstantinos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation presents a comprehensive study of modeling and control issues associated with nonholonomic differential drive mobile robots. The first part of dissertation focuses on modeling using Lagrangian mechanics. The dynamics is modeled as a two-input two-output (TITO) nonlinear model. Motor dynamics are also modeled. Trade studies are conducted to shed light on critical vehicle design parameters, and how they impact static properties, dynamic properties, directional stability, coupling and overall vehicle design. An aspect ratio based dynamic decoupling condition is also presented. The second part of dissertation addresses design of linear time-invariant (LTI), multi-input multi-ouput (MIMO) fixed-structure H∞ controllers for the inner-loop velocity (v, ω) tracking system of the robot, motivated by a practical desire to design classically structured robust controllers. The fixed-structure H∞-optimal controllers are designed using Generalized Mixed Sensitivity(GMS) methodology to systematically shape properties at distinct loop breaking points. The H∞-control problem is solved using nonsmooth optimization techniques to compute locally optimal solutions. Matlab’s Robust Control toolbox (Hinfstruct and Systune) is used to solve the nonsmooth optimization. The dissertation also addresses the design of fixed-structure MIMO gain-scheduled H∞ controllers via GMS methodology. Trade-off studies are conducted to address the effect of vehicle design parameters on frequency and time domain properties of the inner-loop control system of mobile robot. The third part of dissertation focuses on the design of outer-loop position (x, y, θ) control system of mobile robot using real-time model predictive control (MPC) algorithms. Both linear time-varying (LTV) MPC and nonlinear MPC algorithms are discussed.The outer-loop performance of mobile robot is studied for two applications - 1) single robot trajectory tracking and multi-robot coordination in presence of obstacles, 2) maximum progress maneuvering on racetrack. The dissertation specifically addresses the impact of variation of c.g. position w.r.t. wheel-axle on directional maneuverability, peak control effort required to perform aggressive maneuvers, and overall position control performance. Detailed control relevant performance trade-offs associated with outer-loop position control are demonstrated through simulations in discrete time. Optimizations packages CPLEX(convex-QP in LTV-MPC) and ACADO(NLP in nonlinear-MPC) are used to solve the OCP in real time. All simulations are performed on Robot Operating System (ROS).
ContributorsMondal, Kaustav (Author) / Rodriguez, Armando A (Thesis advisor) / Berman, Spring M (Committee member) / Si, Jenni (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to their effectiveness in capturing similarities between different entities, graphical models are widely used to represent datasets that reside on irregular and complex manifolds. Graph signal processing offers support to handle such complex datasets. By extending the digital signal processing conceptual frame from time and frequency domain to graph domain, operators such as graph shift, graph filter and graph Fourier transform are defined. In this dissertation, two novel graph filter design methods are proposed. First, a graph filter with multiple shift matrices is applied to semi-supervised classification, which can handle features with uneven qualities through an embedded feature importance evaluation process. Three optimization solutions are provided: an alternating minimization method that is simple to implement, a convex relaxation method that provides a theoretical performance benchmark and a genetic algorithm, which is computationally efficient and better at configuring overfitting. Second, a graph filter with splitting-and-merging scheme is proposed, which splits the graph into multiple subgraphs. The corresponding subgraph filters are trained parallelly and in the last, by merging all the subgraph filters, the final graph filter is obtained. Due to the splitting process, the redundant edges in the original graph are dropped, which can save computational cost in semi-supervised classification. At the same time, this scheme also enables the filter to represent unevenly sampled data in manifold learning. To evaluate the performance of the proposed graph filter design approaches, simulation experiments with synthetic and real datasets are conduct. The Monte Carlo cross validation method is employed to demonstrate the need for the proposed graph filter design approaches in various application scenarios. Criterions, such as accuracy, Gini score, F1-score and learning curves, are provided to analyze the performance of the proposed methods and their competitors.
ContributorsFan, Jie (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2022
Description新世纪以来中国电影的产业化改革与探索愈发呈现良好的态势，国产院线电影也在实践中努力赢得观众和票房市场。其中类型喜剧电影，最符合商业电影规律、最顺应影视市场需求、最能获得票房收益而备受影视创投机构、制作公司青睐。本论文研究对象聚焦类型喜剧电影，通过“欢声笑语里的财富”现象，探究类型喜剧电影内部本体构成要素与外部客观促成要素的关联；以通过分析自变量与因变量因素对中国电影票房之类型喜剧影响因素进行实证研究，为影视创投和影视制作总结并提供可靠建议。
本论文整体结构包括：第一部分为导论，包括研究背景、目的意义，相关文献综述与文献评述和论文创新性。第二部分聚焦类型喜剧本身，从电影学范畴的电影本体出发，探究“笑”的心理、社会与文化内涵，并分析将“笑”对经济领域的延伸。第三部分以影视投资、票房为依托，从现象和数据中探寻影响类型喜剧电影的因素，为展开中国电影票房之类型喜剧影响因素实证研究做好理论的铺垫。第四与第五部分则基于上述理论进行实证检验，选用2013-2020年电影样本，采用多元线性回归模型研究喜剧类型对票房的吸引力，以及不同种类型喜剧对电影票房的提振效果作用差异。研究发现喜剧电影对电影票房有显著的提振作用；以及研究电影的外部影响因素（续集效应）对电影票房的作用。发现续集电影有更好的票房表现，续集效应的票房提升作用在喜剧电影中表现的更加明显。
本论文研究成果最终将回归到“欢声笑语里的财富”本身；即“类型复合喜剧”对促进电影与金融产业的互动关联、实现更加可持续化发展，以及进而推动经济及文化业的发展。
ContributorsLiu, Yongqian (Author) / Shen, Wei (Thesis advisor) / Zhu, Ning (Thesis advisor) / Dong, Xiaodan (Committee member) / Arizona State University (Publisher)
Created2022
Description人口的老龄化不仅对养老事业提出更高的要求，也对养老服务产业人才的培养提出要求。但是青年学生选择涉老服务专业的意愿却非常低。因此，为了探究职业学院如何增强涉老服务专业吸引力这一问题，本文以学生为主体视角，利用相关理论，对于影响青年学生选择涉老服务专业的因素进行全面的分析，并结合深度访谈和调查法，提出并建构了相关的理论模型。首先，通过深度访谈和焦点小组讨论，结合对现有的文献的分析，本文提出了影响青年学生选择职业院校涉老服务专业的各种因素，主要包括：个人未来风险感知、家庭经济资本、社会信息评价、校企合作水平、专业课程建设水平、学生激励水平、师资队伍建设水平。之后，本文通过调查法，基于社会认同理论构建了本文的研究模型，并通过结构方程模型对所构建的模型进行检查。
本文的研究结果表明：个人未来风险感知对学生专业认同度产生负面影响；家庭经济资本对学生专业认同度产生负面影响；社会信息评价对学生专业认同度产生正面影响；校企合作水平对学生专业认同度产生正面影；专业课程建设水平对学生专业认同度产生正面影响；学生激励水平对学生专业认同度产生正面影响；师资队伍建设水平对学生专业认同度产生正面影响；学生专业认同度对学生专业选择意愿产生正面影响。
基于上述研究结论，本文选取了个人未来风险感知、家庭经济资本、社会信息评价、校企合作水平、专业课程建设水平、学生激励水平、师资队伍建设水平等因素对于广东岭南职业技术学院涉老服务专业的现有吸引力进行了分析和评估，并从这些视角进一步了对如何提升招生吸引力问题进行探讨，为提高涉老服务专业对于青年学生的吸引力，得出了相关管理建议。
ContributorsZhou, Lanqing (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Pei, Ker-Wei (Committee member) / Arizona State University (Publisher)
Created2021
Description
Operational efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of photovoltaic (PV) arrays under various conditions. This dissertation describes a project that focuses on the development of machine learning and neural network algorithms. It also describes an 18kW solar array testbed for the purpose of PV monitoring and control. The use of the 18kW Sensor Signal and Information Processing (SenSIP) PV testbed which consists of 104 modules fitted with smart monitoring devices (SMDs) is described in detail. Each of the SMDs has embedded, a wireless transceiver, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. Data is obtained in real time using the SenSIP PV testbed. Machine learning and neural network algorithms for PV fault classification is are studied in depth. More specifically, the development of a series of customized neural networks for detection and classification of solar array faults that include soiling, shading, degradation, short circuits and standard test conditions is considered. The evaluation of fault detection and classification methods using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN) is performed. The examination and assessment the classification performance of customized neural networks with dropout regularizers is presented in detail. The development and evaluation of neural network pruning strategies and illustration of the trade-off between fault classification model accuracy and algorithm complexity is studied. This study includes data from the National Renewable Energy Laboratory (NREL) database and also real-time data collected from the SenSIP testbed at MTW under various loading and shading conditions. The overall approach for detection and classification promises to elevate the performance and robustness of PV arrays.
ContributorsRao, Sunil (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation examines modeling, design and control challenges associatedwith two classes of power converters: a direct current-direct current (DC-DC) step-down (buck)
regulator and a 3-phase (3-ϕ) 4-wire direct current-alternating current
(DC-AC) inverter. These are widely used for power transfer in a variety of industrial
and personal applications. This motivates the precise quantification of conditions
under which existing modeling and design methods yield satisfactory designs, and
the study of alternatives when they don’t. This dissertation describes a method
utilizing Fourier components of the input square wave and the inductor-capacitor (LC)
filter transfer function, which doesn’t require the small ripple approximation. Then,
trade-offs associated with the choice of the filter order are analyzed for integrated buck
converters with a constraint on their chip area. Design specifications which would
justify using a fourth or sixth order filter instead of the widely used second order
one are examined. Next, sampled-data (SD) control of a buck converter is analyzed.
Three methods for the digital controller design are studied: analog design followed
by discretization, direct digital design of a discretized plant, and a “lifting” based
method wherein the sampling time is incorporated in the design process by lifting
the continuous-time design plant before doing the controller design. Specifically,
controller performance is quantified by studying the induced-L2 norm of the closed
loop system for a range of switching/sampling frequencies. In the final segment of
this dissertation, the inner-outer control loop, employed in inverters with an
inductor-capacitor-inductor (LCL) output filter, is studied. Closed loop sensitivities for the
loop broken at the error and the control are examined, demonstrating that traditional
methods only address these properties for one loop-breaking point. New controllers
are then provided for improving both sets of properties.
ContributorsSarkar, Aratrik (Author) / Rodriguez, Armando A (Thesis advisor) / Si, Jennie (Committee member) / Mittelmann, Hans D (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2021