International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper discusses the design and implementation of a microcomputer system that functions as the central processing unit for performing servo system control, tracking mode determination, operator interface, switching, and logic operations. The computer hardware consists of VMEbus compatible boards that include a Motorola 32-bit MC68020 microprocessor-based CPU board, and a variety of interface boards. The computer is connected to the Radio Frequency system, Antenna Control Unit, azimuth and elevation servo systems, and other systems of the Advanced Transportable Telemetry Acquisition System (TTAS-A) through extensive serial, analog, and digital input/output interfacing. The software platform consists of a commercially-acquired real-time multi-tasking operating system, and in-house developed device drivers and tracking system software. The operating system kernel is written in assembly language, while the application software is written using the C programming language. To enhance the operation of the TTAS-A, software was also developed to provide color graphics, CRT menus, printer listings, interactive real-time hardware/software diagnostics, and a GPIB (IEEE-488 bus) interface for Automated Testing System support.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / To meet challenging constraints on telemetry system weight and volume, a custom Light-Weight Instrumentation System was developed to collect vehicle environment and dynamics on a short-duration exo-atmospheric flight test vehicle. The total telemetry system, including electronics, sensors, batteries, and a 1 watt transmitter weighs about 1 kg. Over 80 channels of measurement, housekeeping, and telemetry system diagnostic data are transmitted at 128 kbps. The microcontroller-based design uses the automotive industry standard Controller Area Network to interface with and support in-flight control functions. Operational parameters are downloaded via a standard asynchronous serial communications interface. The basic design philosophy and functionality is described here.

Understanding and synthesizing locomotion of humans and animals will have far-reaching impacts in computer animation, robotic and biomechanics. However, due to the complexity of the neuromuscular control and physical interactions with the environment, computationally modeling these seemingly effortless locomotion imposes a grand challenge for scientists, engineers and artists. The focus of this thesis is to present a set of computational tools, which can simulate the physical environment and optimize the control strategy, to automatically synthesize locomotion for humans and animals. We first present computational tools to study swimming motions for a wide variety of aquatic animals. This method first builds a simulation of two-way interaction between fluid and an articulated rigid body system. It then searches for the most energy efficient way to swim for a given body shape in the simulated hydrodynamic environment. Next, we present an algorithm that can synthesize locomotion of soft body animals that do not have skeleton support. We combine a finite element simulation with a muscle model that is inspired by muscular hydrostat in nature. We then formulate a quadratic program with complementarity condition (QPCC) to optimize the muscle contraction and contact forces that can lead to meaningful locomotion. We develop an efficient QPCC solver that solves a challenging optimization problem at the presence of discontinuous contact events. We also present algorithms to model human locomotion with a passive mechanical device: riding a bicycle in this case. We apply a powerful reinforcement learning algorithm, which can search for both the parametrization and the parameters of a control policy, to enable a virtual human character to perform bicycle stunts in a physically simulated environment. Finally, we explore the possibility to use the computational tools that are developed for computer animation to control a real robot. We develop a simulation calibration technique which reduces the discrepancy between the simulated results and the performance of the robot in the real environments. For certain motion planning tasks, this method can transfer the controllers optimized for a virtual character in a simulation to a robot that operates in a real environment.

The Internet faces well-known challenges in realizing modifications to the core architecture. To help overcome these limitations, the use of network virtualization has been proposed. Network virtualization enables the deployment of novel network architectures and services on existing Internet infrastructure. Virtual networks run over physical networks and use Internet paths and protocols as essentially a link layer in the virtual network. Virtual networks can also share the resources in the physical substrate. Effective use of the underlying substrate network requires intelligent placement of virtual networks so that underlying resources do not incur over-subscription. Because virtual networks can come and go over time, and underlying networks can experience their own dynamic changes, virtual networks need to be migrated---re-mapped to the physical network during active operation---to maintain good performance. While virtual network placement, and to a lesser extent migration, has been studied in the past, little attention has been devoted to designing, deploying, and evaluating migration mechanisms for virtual networks. In this dissertation, we design virtual network migration mechanisms for different substrate platforms and further design a system to mitigate the effects of virtual network migration. In particular this dissertation makes the following contributions: 1. With the goal of minimizing the disruption during a virtual network migration, we design three algorithms for scheduling the sequence of virtual router moves that takes a virtual network from its original placement to its new placement. 2. We design and implement a controller-based architecture for virtual network migration on PlanetLab. This work explores the challenges in implementing virtual network migration on real infrastructure. Recommendations are given for infrastructure that support virtual network migration. 3. We propose and implement a mechanism to mitigate the performance degradation resulting from virtual network migration through transport and application layer collaboration. We utilize a centralized controller to notify the end-systems or the gateways about the time of the virtual network migration such that we prevent packet loss to the application traffic of the end-systems.

This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. Towards this goal,we design and implement two auto-scaling algorithms for cloud infrastructures. Thealgorithms predict the future load for an application running in the cloud. We discussthe different approaches to designing an auto-scaler combining reactive and proactivecontrol methods, and to be able to handle long running requests, e.g., tasks runningfor longer than the actuation interval, in a cloud. We compare the performance ofour algorithms with state-of-the-art auto-scalers and evaluate the controllers’ perfor-mance with a set of workloads. As any controller is designed with an assumptionon the operating conditions and system dynamics, the performance of an auto-scalervaries with different workloads.In order to better understand the workload dynamics and evolution, we analyze a6-years long workload trace of the sixth most popular Internet website. In addition,we analyze a workload from one of the largest Video-on-Demand streaming servicesin Sweden. We discuss the popularity of objects served by the two services, the spikesin the two workloads, and the invariants in the workloads. We also introduce, a mea-sure for the disorder in a workload, i.e., the amount of burstiness. The measure isbased on Sample Entropy, an empirical statistic used in biomedical signal processingto characterize biomedical signals. The introduced measure can be used to charac-terize the workloads based on their burstiness profiles. We compare our introducedmeasure with the literature on quantifying burstiness in a server workload, and showthe advantages of our introduced measure.To better understand the tradeoffs between using different auto-scalers with differ-ent workloads, we design a framework to compare auto-scalers and give probabilisticguarantees on the performance in worst-case scenarios. Using different evaluation cri-teria and more than 700 workload traces, we compare six state-of-the-art auto-scalersthat we believe represent the development of the field in the past 8 years. Knowingthat the auto-scalers’ performance depends on the workloads, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components;an analyzer, and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads, and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

This Masters research proposes a new system which deals with the management of renewable energy sources in a domestic/commercial small scale environment. The aim of the project is to develop an intelligent system which will monitor current in individual circuit loads in a domestic/commercial environment and establish whether the load can be powered from mains supply or be switched to an alternative energy supply in a dynamic way. The alternative energy can be solar energy from photovoltaic panels, wind generators or hydro generation. The switching between supplies is decided by monitoring load currents using a microcontroller and the switching action is taken only at specic allowed instants. The CAN (Controller Area Network)communication system is a two-wire differential serial bus system, developed by Bosch for automotive applications in the early 1980s. Its reliability and robustness in communication between nodes within the control system are the reasons for its popularity. The CAN system is implemented in the Eco Energy Controller. The prototype of the Eco Energy Controller is operational and has been tested with 6 resistive load, 24mH inductive load, and three 25W incandescent light bulbs. Experimental measurements and waveforms indicate that the prototype is successful in switching between two supplies to each of the loads without causing high current peaks during turn on.

This thesis introduces locomotion synthesis methods for humanoid characters. Motion synthesis is an under-constrained problem that requires additional constraints beyond user inputs. Two main approaches to introducing additional constraints are physics-based and data-driven. Despite significant progress in the past 20 years, major difficulties still exist for both approaches. In general, building animation systems that are flexible to user requirements while keeping the synthesized motions plausible remain a challenging task. The methods introduced in this thesis, presented in two-parts, aim to allow animation systems to be more flexible to user demands without radically violating constraints that are important for maintaining plausibility. In the first part of the thesis, we address an important subproblem in physics-based animation --- controller synthesis for humanoid characters. We describe a method for optimizing the parameters of a physics-based controller for full-body, 3D walking. The objective function includes terms for power minimization, angular momentum minimization, and minimal head motion, among others. Together these terms produce a number of important features of natural walking, including active toe-off, near-passive knee swing, and leg extension during swing. We then extend the algorithm to optimize for robustness to uncertainty. Many unknown factors, such as external forces, control torques, and user control inputs, cannot be known in advance and must be treated as uncertain. Controller optimization entails optimizing the expected value of the objective function, which is computed by Monte Carlo methods. We demonstrate examples with a variety of sources of uncertainty and task constraints. The second part of this thesis deals with the data-driven approach and the problem of motion modeling. Defining suitable models for human motion data is non-trivial. Simple linear models are not expressive enough, while more complex models require setting many parameters and are difficult to learn with limited data. Using Bayesian methods, we demonstrate how the Gaussian process prior can be used to derive a kernelized version of multilinear models. The result is a locomotion model that takes advantage of training data addressed by multiple indices to improve generalization to unseen motions.

The industrial world of robotics requires leading-edge controllers to match the speed of new manipulators. At the University of Waterloo, a three degree-of-freedom ultra high-speed cable-based robot was created called Deltabot. In order to improve the performance of the Deltabot, a new controller called the QNX Multi-Axis Robotic Controller (QMARC) was developed. QMARC is a PC-based controller built for the replacement of the existing commercial controller called PMAC, manufactured by Delta Tau Data Systems. Although the PMAC has its own real-time processor, the rigid and complex internal structure of the PMAC makes it difficult to apply advanced control algorithms and interpolation methods. Adding unconventional hardware to PMAC, such as a camera and vision system is also quite challenging. With the development of QMARC, the flexibility issue of the controller is resolved. QMARC?s open-sourced object-oriented software structure allows the addition of new control and interpolation techniques as required. In addition, the software structure of the main Controller process is decoupled for the hardware, so that any hardware change does not affect the main controller, just the hardware drivers. QMARC is also equipped with a user-friendly graphical user interface, and many safety protocols to make it a safe and easy-to-use system. <br /><br /> Experimental tests has proven QMARC to be a safe and reliable controller. The stable software foundation created by the QMARC will allow for future development of the controller as research on the Deltabot progresses.

This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.

Samhällets teknikutveckling har bidragit till att organisationer och yrkesroller förändras. En av de yrkesroller som förändras och påverkas av IT-lösningars nya möjligheter är controllern. Det finns en brist på studier som undersöker teknikens inverkan på controllerrollen och dess arbetsuppgifter. Följaktligen är studiens syfte att undersöka hur IT-lösningar möjliggör controllerns roll och arbete. För att undersöka detta genomfördes semistrukturerade intervjuer med sex stycken verksamma controllrar. Studiens resultat visar att välfungerande IT-lösningar kan möjliggöra för controllern att lägga mindre tid på produktionsbaserat arbete, något som länge karaktäriserat controllerrollen och dess arbete, och istället mer tid på analys och kommunikation. Dock utnyttjas inte alltid IT-lösningars fulla kapacitet, dels på grund av att de inte är användarvänligt utformade, dels för att kunskapen om vissa av dem hos controllrar i nuläget tycks vara relativt grund. Därmed sker det en utvecklingsprocess för IT-lösningars möjliggörande för controllerns roll och arbete.