This thesis examines a manufacturing process using a real time interface with a 6502 microprocessor that gives the appearance of parallel processing. Two separate processes are operated, apparently simultaneously with an asynchronous interface between the two processes. An Apple microcomputer, an ISAAC data transfer system and a constructed simulation model are used to demonstrate this process. The model is constructed of Fischertechnik manufactured parts for the support framework, as well as gearing devices, small DC electric motors, and sensing devices in the form of photo-electric switches and single pole double throw switches physically activated by the constructed model. The software, written in Applesoft BASIC and Cyborg's Labsoft, was designed to operate the modeled processes simultaneously and allow an asynchronous interaction between the two processes. The model has applications for use as a method to illustrate manufacturing techniques and to assist in the design and control of manufacturing processes.

Deregulation of electricity markets, increased usage of intermittent energy sources, and growing environmental concerns have created a volatile process manufacturing environment. Survival under this new paradigm requires chemical manufactures to shift from the traditional steady-state operation to a more dynamic and flexible operation mode. Under more frequent operating changes, the transition dynamics become increasingly relevant, rendering the traditional steady-state based scheduling decision-making suboptimal. This has motivated calls for the integration of scheduling and control. In an integrated scheduling and control framework, the scheduling decisions are based on a dynamic representation of the process. While various integration paradigms are proposed in the literature, our study concentrates on the closed-loop integration of scheduling and control. There are two main advantages to this approach: (i) seamless integration with the existing control system (i.e. it does not require a new control system infrastructure), (ii) the framework is aware of the control system dynamics, and hence has knowledge of the closed-loop process dynamics. The later aspect is particularly important as the control system plays a key role in determining the transition dynamics. The first part of our work is dedicated to developing an integrated scheduling and control framework that computes set-point trajectories, to be tracked by the lower-level linear model predictive control system, that are robust to demand uncertainty. We employ a piecewise linear representation of the nonlinear process model to obtain a mixed-integer linear programming (MILP) problem, thus alleviating the computational complexity compared to a mixed-integer nonlinear programming formulation. The value of the stochastic solution is used to confirm the superiority of the robust formulation against a nominal one that disregards uncertainty. In the second part of this study, we expand the framework to accommodate additional uncertainty types, including model and cost uncertainty. In the third part of this thesis, a deterministic integrated scheduling and control framework for processes controlled by distributed linear model predictive control is developed. The integrated problem is formulated as a MILP. To reduce the solution time, we introduce strategies to approximate the feedback control action. Through case studies, we demonstrate that knowledge of the control system enables the framework to effectively coordinate the MPC subsystems. The framework performs well even under conditions of plant-model mismatch conditions. In the final part of this study, we introduce an integrated scheduling and control formulation for processes controlled by nonlinear model predictive control (NMPC). Here, discrete scheduling decisions are represented using complementarity conditions. Additionally, we use the first-order Karush-Kuhn-Tucker conditions of the NMPC controller to compute the input values in the integrated problem. The resulting problem is a mathematical program with complementarity constraints that we solve using a regularization approach. For all case studies, the complementarity formulation effectively capture discrete scheduling decisions, and the KKT conditions always provides a local optimum of the associated NMPC problem. In summary, this study of the integration of scheduling and control addresses various control systems, uncertainty, and strategies for enhancing the solution time. Furthermore, we assess the performance of the proposed frameworks under conditions of plant-model mismatch, a common scenario in real-life applications. / Thesis / Doctor of Philosophy (PhD)

In real-word manufacturing environments, finding the right job sequences and their associated schedules when resource, precedence, and timing constraints are imposed is a difficult task. For most practical problems classical scheduling easily leads to an exponential growth in the number of possible schedules. Moreover, a decision time period of hours or even minutes is too long. Good solutions are often needed in real-time. The problem becomes even more complicated if changes, such as new orders or resource breakdowns, occur within the manufacturing system. One approach to overcome the challenges of solving classical scheduling problems is the use of distributed schemes such as agent or holonic-based control architectures. This dissertation presents an innovative control architecture that uses the holonic concept, capable of delivering good solutions when applied in dynamic environments. The general holonic control framework presented in this research has specific characteristics not found in others reported so far. Using a modular approach it takes into account all the categories of hardware and software resources of a manufacturing system. Due to its modularity, the holonic control framework can be used for assigning and scheduling different task types, separately or simultaneously. Thus, it can be used not only for assigning and scheduling transport tasks, but also for finding feasible solutions to the job assignment and scheduling of processing tasks, or to better utilize the auxiliary equipment and devices in a manufacturing system. In the holonic system, under real-time constraints, a feasible schedule for the material handling resources emerges from the combination of individual holon's schedules. Internal evaluation algorithms and coordination mechanisms between the entities in the architecture form the basis for the resultant schedules. The experimental results obtained show a percentage difference between the makespan values obtained using the holonic scheduling approach and the optimal values of under seven percent. Since current control systems in use in industry lack the ability to adapt to dynamic manufacturing environments, the holonic architecture designed and the tests performed in this research could be a part in the effort to build the foundations for the control systems of the next generation manufacturing systems. / Ph. D.

Utility Accrual (UA) scheduling is a method of dynamic real-time scheduling that is designed to respond to overload conditions by producing a feasible schedule that heuristically maximizes a pre-defined metric of utility. Whereas utility accrual schedulers have traditionally focused on CPU overload, this dissertation explores memory overload conditions during which the aggregate memory demand exceeds a system's available memory bandwidth. Real-time systems are typically implemented in C or other languages that use explicit dynamic memory management. Taking advantage of modern type-safe languages, such as Java, necessitates the use of garbage collection (GC). The timeliness requirements of real-time systems, however, impose specific demands on the garbage collector. Garbage collection introduces a significant source of unpredictability in the execution timeline of a task because it unexpectedly interjects pauses of arbitrary length, at arbitrary points in time, with an arbitrary frequency. To construct a feasible schedule, a real-time scheduler must have the ability to predict the collector's activities and plan for them accordingly. We have devised CADUS (Collector-Aware Dynamic Utility Scheduler), a utility accrual algorithm that tightly links CPU scheduling with the memory requirements -and the corresponding garbage collection activities - of real-time tasks. By constructing and storing memory time allocation profiles, we address the problem of GC activation strategy. We estimate GC latency by using a real-time collector and modeling its behavior. We project GC frequency by planning, at schedule construction time, the memory bandwidth available to the collector. CADUS can point the collector's activities to any specific task in the system. The runtime system provides this ability by maintaining separate logical heaps for all tasks. We demonstrate the viability of CADUS through extensive simulation studies. We evaluated the behavior of CADUS under a wide range of CPU and memory load conditions and utility distributions. We compared its performance against an existing GC-unaware UA scheduler and found that CADUS consistently outperformed its GC-unaware counterpart. We investigated and identified the reasons for the superior performance of CADUS and quantified our results. Most significantly, we found that in an overloaded dynamic soft real-time system, a scheduler's preemption decisions have a highly significant impact on GC latency. A dynamic real-time scheduler therefore must predict the impact of its preemption decisions on GC latency in order to construct time-feasible schedules. / Ph. D.

Due to close coupling to hardware and lack of software engineering technologies, the control software in digitally controlled power conversion systems is difficult to design and maintain. This is a natural consequence of a topology- or application-driven design approach. This research work proposes a distributed, modular, open control architecture for power conversion systems to reduce control design complexity, encapsulate and localize design dependencies, reduce unnecessary redesign effort and improve software quality. Dataflow style is chosen as the architectural style for the proposed control architecture based on comparative analysis. The detailed implementation of the dataflow architecture is presented. The resulting dataflow control software is evaluated in comparison to the legacy approach to control design used in industry and academia. The dataflow control software for a 3-phase voltage source inverter is also tested on a real PEBB-based converter system. To further explore the flexibility of control composition that is brought by the dataflow approach, the feasibility of dynamic control reconfiguration is also presented as an important future research direction. / Ph. D.

While increasing number of vehicles on urban roadways create uncontrolled congestion, connectivity among vehicles, traffic lights and other road-side units provide abundant data that paves avenues for novel smart traffic control mechanisms to mitigate traffic congestion and delays. However, increasingly complex vehicular applications have outpaced the computational capabilities of on-board processing units, therefore requiring novel offloading schemes onto additional resources located by the road-side. Adding connectivity and other computational resources on legacy traffic infrastructure may also introduce security vulnerabilities. To ensure that the timeliness and resource constraints of the vehicles using the roadways as well as the applications being deployed on the traffic infrastructure are met, the transportation systems needs to be more predictable. This dissertation discusses three areas that focus on improving the predictability and performance of the connected traffic infrastructure. Firstly, a holistic traffic control strategy is presented that ensures predictable traffic flow by minimizing traffic delays, accounting for unexpected traffic conditions and ensuring timely emergency vehicle traversal through an urban road network. Secondly, a vehicular edge resource management strategy is discussed that incorporates connected traffic lights data to meet timeliness requirements of the vehicular applications. Finally, security vulnerabilities in existing traffic controllers are studied and countermeasures are provided to ensure predictable traffic flow while thwarting attacks on the traffic infrastructure. / Doctor of Philosophy / Exponentially increasing vehicles especially in urban areas create pollution, delays and uncontrolled traffic congestion. However, improved traffic infrastructure brings connectivity among the vehicles, traffic lights, road-side detectors and other equipment, which can be leveraged to design new and advanced traffic control techniques. The initial work in this dissertation provides a traffic control technique that (i) reduces traffic wait times for the vehicles in urban areas, (ii) ensures safe and quick movements of emergency vehicles even through crowded areas, and (iii) ensures that the traffic keeps moving even under unexpected lane closures or roadblocks. As technology advances, connected vehicles are becoming increasingly automated. This allows the car manufacturers to design novel in-vehicle features where the passengers can now stream media-rich content, play augmented reality (AR)-based games and/or get high definition information about the surroundings on their car's display, while the car is driven through the urban traffic. This is made possible by providing additional computing resources along the road-side that the vehicles can utilize wirelessly to ensure passenger's comfort and improved experience of in-vehicle features. In this dissertation, a technique is provided to manage the computational resources which will allow vehicles (and its passengers) to use multiple features simultaneously. As the traffic infrastructure becomes increasingly inter-connected, it also allows malicious actors to exploit vulnerabilities such as modifying traffic lights, interfering with road-side sensors, etc. This can lead to increased traffic wait times and eventually bring down the traffic network. In the final work, one such vulnerability in traffic infrastructure is studied and mitigating measures are provided so that the traffic keeps moving even when an attack is detected. In all, this dissertation aims to improve safety, security and overall experience of the drivers, passengers and the pedestrians using the connected traffic infrastructure.

This thesis presents the design and evaluation of an operating system kernel specially designed for dataflow software. Dataflow is a style of software architecture that is well suited for control and "signal flow" applications. This architecture involves many small processes and lots of inter-process communication, which impose too much overhead on traditional RTOSes. This thesis describes design and implementation of the Dataflow Architecture Real-time Kernel (DARK). DARK is a reconfigurable, multithreaded and preemptive operating system kernel that introduces a special data-driven scheduling strategy for dataflow applications. It uses the underlying hardware for high-speed context switching between the kernel and applications, which is five times faster than the ordinary context switch. The features of the kernel can be configured according to performance requirements without change to the applications. Along with the performance evaluation of DARK, the performance comparison results of DARK with two commercial RTOSes: MicroC/OS-II and Analog Devices VDK++ are also provided. / Master of Science

Alcohol use disorder is common, and treatments are currently inadequate. Some of the acute effects of alcohol on the brain, such as altering the decision-making and future thinking capacities, mirror the effects of chronic alcohol use. Therefore, interventions that can address these shortcomings may be useful for reducing the negative effects of alcohol use disorder in combination with other therapies. The signature of those interventions may also be evident in the signature of large-scale, dynamic brain networks, which can show whether an intervention is effective. One such intervention is episodic future thinking, which has been shown to reduce delay discounting and orient people toward pro-social, long-term outcomes. To better understand decision making in high-risk individuals, we examined delay discounting in an adolescent population. When the decision-making faculties were challenged with difficult choices, adolescents made decisions inconsistent with their predicted preference, complemented by increased brain activity in the central executive network and salience network. Using these results and the hypothesis that the default mode network would be implicated in future thinking and intertemporal choice, we examined the neural effects of a brief behavioral intervention, episodic future thinking, that seeks to address these impairments. We showed that episodic future thinking has both acute and longer-lasting effects on consequential brain networks at rest and during delay discounting compared to a control episodic thinking condition in alcohol use disorder. Our failure to show group differences in default mode network prompted us to scrutinize it more carefully, from a position where we could measure the ability to self-regulate the network rather than its resting-state tendency. We implemented a real-time fMRI experiment to test the degree to which people along the alcohol use severity spectrum can self-regulate this network. Our results showed that default mode network suppression is impaired as alcohol use disorder severity increases. In the process, we showed that direct examination of resting-state networks with these methods will provide more information than measuring them at rest alone. We also characterized the default mode network along the real-time fMRI pipeline to show the whole-brain spatial pattern of regions associated and unassociated with the network. Our results indicate that resting-state brain networks are important markers for outcomes in alcohol use disorder and that they can be manipulated under experimental conditions, potentially to the benefit of the afflicted individual. / Doctor of Philosophy / Alcohol is the most widely-used mind-altering substance in the United States. Even though most people do not develop a problem with alcohol use, many people will at some point develop drinking patterns that classify as an alcohol use disorder. Brain damage from drinking can come from the toxicity of alcohol, but also as a result of behaviors associated with drinking too much, including injury, violence, accidents, and other health-related issues. Interventions at the behavioral level can be effective at curbing drinking patterns before damage accrues, and a better understanding of those interventions at the level of the brain may make them more effective. This work investigated the decision-making processes and the ability to think clearly about the future, two faculties that begin to become diminished in alcohol use disorder. In our first set of studies, we tested a brief behavioral intervention called episodic future thinking, which helps people orient themselves away from short-term rewards like alcohol and toward long-term goals that could happen if they stopped drinking as much. We showed that one hour-long, intensive session produced changes in the connectivity between the prefrontal cortex and the lower brain. We also generated data in a long-term experiment suggesting repeated reminders of the episodic future thinking intervention produce changes in large-scale brain networks that are disrupted in substance use disorders. In a separate set of experiments, we showed that people can gain control over one of these networks, called the default mode network, to the point of being able to control a brain-machine interface just by following simple instructions. However, we demonstrated that the degree to which someone can control this brain activity was associated with their drinking severity. In other words, the more people drank, in terms of volume and frequency, the less control they had over their own brain activity. This finding is important because many researchers have shown that activity in this brain region is related to many psychopathologies, including substance use disorders. Other researchers have been developing ways in which the ability to control this brain activity can be trained. While we did not find evidence of a training effect in a small group of healthy people (5), it may be the case that people impaired by alcohol use disorder can improve through practice or through cutting back on drinking. Ultimately, we hope that the research presented here will help to guide the development of treatments for alcohol use disorder to be more effective.

This thesis presents the design and evaluation of an object-oriented (OO) operating system kernel for real-time embedded systems based on dataflow architecture. Dataflow is a software architecture that is well suited to applications that involve signal flows and value transformations. Typically, these systems comprise numerous processes with heavy inter-process communications. The dataflow style has been adopted for the control software for PEBB (Power Electronic Building Block) systems by the Center for Power Electronic Systems (CPES), Virginia Tech., which is involved in a research effort to modularize and standardize power electronic components. The goal of our research is to design and implement an efficient object-oriented kernel for the PEBB system and compare its performance vis-à-vis that of a non-OO kernel. It presents strategies for efficient OO design and a discussion of how OO performance issues can be ameliorated. We conclude the thesis with an evaluation of the advantages gained by using the OO paradigm both from the standpoint of the classically cited advantages of OO programming and other crucial aspects. / Master of Science

This research presents the development and proof of concept of a TeleGeoMonitoring (TGM) system for spatially monitoring and analyzing, in real-time, data derived from vehicle-mounted sensors. In response to the concern for vibration related injuries experienced by equipment operators in surface mining and construction operations, the prototype TGM system focuses on spatially monitoring vehicle vibration in real-time. The TGM vibration system consists of 3 components: (1) Data Acquisition Component, (2) Data Transfer Component, and (3) Data Analysis Component. A GPS receiver, laptop PC, data acquisition hardware, triaxial accelerometer, and client software make up the Data Acquisition Component. The Data Transfer Component consists of a wireless data network and a data server. The Data Analysis Component provides tools to the end user for spatially monitoring and analyzing vehicle vibration data in real-time via the web or GIS workstations. Functionality of the prototype TGM system was successfully demonstrated in both lab and field tests. The TGM vibration system presented in this research demonstrates the potential for TGM systems as a tool for research and management projects, which aim to spatially monitor and analyze data derived from mobile sensors in real-time. / Master of Science