Distributed Supervisory Controller Design for Battery Swapping Modularity in Plug-in Hybrid Electric Vehicles.

As industry strives to standardize engineering design, manufacturing, and maintenance processes, the focus on achieving component modularity is increasing. Component swapping modularity (CSM) in control systems allows component change without redesign of the system level controller, while achieving the required system performance. Opportunities to achieve CSM are emerging in control systems consisting of smart components connected by bidirectional communication networks. By distributing a part of the controller into the component module, controller recalibration can be limited to only the component module when the component changes. In this dissertation, a novel Direct Method is proposed to generate the distributed controller with CSM through a bi-level optimization. The distributed controller enables CSM and provides required system performance for each component variant. The Direct Method is applied to throttle actuator CSM design in engine idle speed control. The results demonstrate that the new Direct Method improves the CSM results compared to the previous 3-Step Method. In addition, the Direct Method permits the designer to trade off desired system performance versus achievable CSM. The Direct Method is then applied to design a distributed supervisory controller for battery CSM in plug-in hybrid electric vehicles. A novel feedback based controller for the charge sustaining mode is proposed. For effective controller distribution, a method based on sensitivity analysis of the control signals with respect to the battery hardware parameter is introduced. The bi-level optimization problem for the distributed controller gains is solved using the Augmented Lagrangian Decomposition method. The results demonstrate that battery CSM can be achieved without compromising fuel economy compared to the centralized control case.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86264/1/sfli_1.pd

Distributed Model-based Control for Gas Turbine Engines

Controlling a gas turbine engine is a fascinating problem. As one of the most complex systems developed, it relies on thermodynamics, fluid mechanics, materials science as well as electrical, control and systems engineering. The evolution of gas turbine engines is marked with an increase in the number of actuators. Naturally, this increase in actuation capability has also been followed by the improvement of other technologies such as advanced high-temperature and lighter materials, improving the efficiency of the aero engines by extending their physical limits. An improvement in the way to control the engine has to be undertaken in order for these technological improvements to be fully harnessed. This starts with the selection of a novel control system architecture and is followed by the design of new control techniques. Model-based control methods relying on distributed architectures have been studied in the past for their ability to handle constraints and to provide optimal control strategies. Applying them to gas turbine engines is interesting for three main reasons. First of all, distributed control architectures provide greater modularity during the design than centralized control architectures. Secondly, they can reduce the life cycle costs linked to both the fuel burnt and the maintenance by bringing optimal control decisions. Finally, distributing the control actions can increase flight safety through improved robustness as well as fault tolerance. This thesis is concerned with the optimal selection of a distributed control system architecture that minimizes the number of subsystem to subsystem interactions. The control system architecture problem is formulated as a binary integer linear programming problem where cuts are added to remove the uncontrollable partitions obtained. Then a supervised-distributed control technique is presented whereby a supervisory agent optimizes the joint communication and system performance metrics periodically. This online optimal technique is cast as a semi-definite programming problem including a bilinear matrix equality and solved using an alternate convex search. Finally, an extension of this online optimal control technique is presented for non-linear systems modelled by linear parameter-varying models

Autonomous Close Formation Flight of Small UAVs Using Vision-Based Localization

As Unmanned Aerial Vehicles (UAVs) are integrated into the national airspace to comply with the 2012 Federal Aviation Administration Reauthorization Act, new civilian uses for robotic aircraft will come about in addition to the more obvious military applications. One particular area of interest for UAV development is the autonomous cooperative control of multiple UAVs. In this thesis, a decentralized leader-follower control strategy is designed, implemented, and tested from the follower’s perspective using vision-based localization. The tasks of localization and control were carried out with separate processing hardware dedicated to each task. First, software was written to estimate the relative state of a lead UAV in real-time from video captured by a camera on-board the following UAV. The software, written using OpenCV computer vision libraries and executed on an embedded single-board computer, uses the Efficient Perspective-n-Point algorithm to compute the 3-D pose from a set of 2-D image points. High-intensity, red, light emitting diodes (LEDs) were affixed to specific locations on the lead aircraft’s airframe to simplify the task if extracting the 2-D image points from video. Next, the following vehicle was controlled by modifying a commercially available, open source, waypoint-guided autopilot to navigate using the relative state vector provided by the vision software. A custom Hardware-In-Loop (HIL) simulation station was set up and used to derive the required localization update rate for various flight patterns and levels of atmospheric turbulence. HIL simulation showed that it should be possible to maintain formation, with a vehicle separation of 50 ± 6 feet and localization estimates updated at 10 Hz, for a range of flight conditions. Finally, the system was implemented into low-cost remote controlled aircraft and flight tested to demonstrate formation convergence to 65.5 ± 15 feet of separation

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above

MATLAB

A well-known statement says that the PID controller is the "bread and butter" of the control engineer. This is indeed true, from a scientific standpoint. However, nowadays, in the era of computer science, when the paper and pencil have been replaced by the keyboard and the display of computers, one may equally say that MATLAB is the "bread" in the above statement. MATLAB has became a de facto tool for the modern system engineer. This book is written for both engineering students, as well as for practicing engineers. The wide range of applications in which MATLAB is the working framework, shows that it is a powerful, comprehensive and easy-to-use environment for performing technical computations. The book includes various excellent applications in which MATLAB is employed: from pure algebraic computations to data acquisition in real-life experiments, from control strategies to image processing algorithms, from graphical user interface design for educational purposes to Simulink embedded systems

Communication Efficiency in Information Gathering through Dynamic Information Flow

This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Design Development Test and Evaluation (DDT and E) Considerations for Safe and Reliable Human Rated Spacecraft Systems

A team directed by the NASA Engineering and Safety Center (NESC) collected methodologies for how best to develop safe and reliable human rated systems and how to identify the drivers that provide the basis for assessing safety and reliability. The team also identified techniques, methodologies, and best practices to assure that NASA can develop safe and reliable human rated systems. The results are drawn from a wide variety of resources, from experts involved with the space program since its inception to the best-practices espoused in contemporary engineering doctrine. This report focuses on safety and reliability considerations and does not duplicate or update any existing references. Neither does it intend to replace existing standards and policy