Advanced Predictive Control Strategies for More Electric Aircraft

Next generation aircraft designs are incorporating increasingly complex electrical power distribution systems to address growing demands for larger and faster electrical power loads. This dissertation develops advanced predictive control strategies for coordinated management of the engine and power subsystems of such aircraft. To achieve greater efficiency, reliability and performance of a More Electric Aircraft (MEA) design static and dynamic interactions between its engine and power subsystems need to be accounted for and carefully handled in the control design. In the pursued approach, models of the subsystems and preview of the power loads are leveraged by predictive feedback controllers to coordinate subsystem operation and achieve improved performance of the MEA system while enforcing state and input constraints. More specifically, this dissertation contains the following key developments and contributions. Firstly, models representing the engine and power subsystems of the MEA, including their interactions, are developed. The engine is a dual-spool turbojet that converts fuel into thrust out of the nozzle and mechanical power at the shafts. Electrical generators extract some of this power and convert it into electricity that is supplied to a High Voltage DC bus to support connected loads, with the aid of a battery pack for smoothing voltage transients. The control objective in this MEA system is to actuate the engine and power subsystem inputs to satisfy demands for thrust and electrical power while enforcing constraints on compressor surge and bus voltage deviations. Secondly, disturbance rejection, power flow coordination, and anticipation of the changes in power loads are considered for effective MEA control. A rate-based formulation of Model Predictive Control (MPC) allowing for offset free tracking is proposed. Centralized control is demonstrated to result in better thrust tracking performance in the presence of compressor surge constraints as compared to decentralized control. Forecast of changes in the power load allows the control to act in advance and reduce bus voltage excursions. Thirdly, distributed MPC strategies are developed which account for subsystem privacy requirements and differences in subsystem controller update rates. This approach ensures coordination between subsystem controllers based on limited information exchange and exploits the Alternating Direction Method of Multipliers. Simulations demonstrate that the proposed approach outperforms the decentralized controller and closely matches the performance of a fully centralized solution. Finally, a stochastic approach to load preview based on a Markov chain representation of a military aircraft mission is proposed. A scenario based MPC is then exploited to minimized expected performance cost while enforce constraints over all scenarios. Simulation based comparisons indicate that this scenario based MPC performs similarly to an idealized controller that exploits exact knowledge of the future and outperforms a controller without preview.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150003/1/wdunham_1.pd

Resource Management in Constrained Dynamic Situations

Resource management is considered in this dissertation for systems with limited resources, possibly combined with other system constraints, in unpredictably dynamic environments. Resources may represent fuel, power, capabilities, energy, and so on. Resource management is important for many practical systems; usually, resources are limited, and their use must be optimized. Furthermore, systems are often constrained, and constraints must be satisfied for safe operation. Simplistic resource management can result in poor use of resources and failure of the system. Furthermore, many real-world situations involve dynamic environments. Many traditional problems are formulated based on the assumptions of given probabilities or perfect knowledge of future events. However, in many cases, the future is completely unknown, and information on or probabilities about future events are not available. In other words, we operate in unpredictably dynamic situations. Thus, a method is needed to handle dynamic situations without knowledge of the future, but few formal methods have been developed to address them. Thus, the goal is to design resource management methods for constrained systems, with limited resources, in unpredictably dynamic environments. To this end, resource management is organized hierarchically into two levels: 1) planning, and 2) control. In the planning level, the set of tasks to be performed is scheduled based on limited resources to maximize resource usage in unpredictably dynamic environments. In the control level, the system controller is designed to follow the schedule by considering all the system constraints for safe and efficient operation. Consequently, this dissertation is mainly divided into two parts: 1) planning level design, based on finite state machines, and 2) control level methods, based on model predictive control. We define a recomposable restricted finite state machine to handle limited resource situations and unpredictably dynamic environments for the planning level. To obtain a policy, dynamic programing is applied, and to obtain a solution, limited breadth-first search is applied to the recomposable restricted finite state machine. A multi-function phased array radar resource management problem and an unmanned aerial vehicle patrolling problem are treated using recomposable restricted finite state machines. Then, we use model predictive control for the control level, because it allows constraint handling and setpoint tracking for the schedule. An aircraft power system management problem is treated that aims to develop an integrated control system for an aircraft gas turbine engine and electrical power system using rate-based model predictive control. Our results indicate that at the planning level, limited breadth-first search for recomposable restricted finite state machines generates good scheduling solutions in limited resource situations and unpredictably dynamic environments. The importance of cooperation in the planning level is also verified. At the control level, a rate-based model predictive controller allows good schedule tracking and safe operations. The importance of considering the system constraints and interactions between the subsystems is indicated. For the best resource management in constrained dynamic situations, the planning level and the control level need to be considered together.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137128/1/sjinu_1.pd

Predictive Energy Management in Connected Vehicles: Utilizing Route Information Preview for Energy Saving

This dissertation formulates algorithms that use preview information of road terrain and traffic flow for reducing energy use and emissions of modern vehicles with conventional or hybrid powertrains. Energy crisis, long term energy deficit, and more restrictive environmental protection policies require developing more efficient and cleaner vehicle powertrain systems. An alternative to making advanced technology engines or electrifying the vehicle powertrain is utilizing ambient terrain and traffic information in the energy management of vehicles, a topic which has not been emphasized in the past. Today\u27s advances in vehicular telematics and advances in GIS (Geographic Information System), GPS (Global Positioning Systems), ITS (Intelligent Transportation Systems), V2V (Vehicle to Vehicle) communication, and VII (Vehicle Infrastructure Integration ) create more opportunities for predicting a vehicle\u27s trip information with details such as the future road grade, the distance to the destination, speed constraints imposed by the traffic flow, which all can be utilized for better vehicle energy management. Optimal or near optimal decision-making based on this available information requires optimal control methods, whose fundamental theories were well studied in the past but are not directly applicable due to the complexity of real problems and uncertainty in the available preview information. This dissertation proposes the use of optimal control theories and tools including Pontryagin minimum principle, Dynamic Programming (DP) which is a numerical realization of Bellman\u27s principle of optimality, and Model Predictive Control (MPC) in the optimization-based control of hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and conventional vehicles based on preview of future route information. The dissertation includes three parts introduced as follows: First, the energy saving benefit in HEV energy management by previewing future terrain information and applying optimal control methods is explored. The potential gain in fuel economy is evaluated, if road grade information is integrated in energy management of hybrid vehicles. Real-world road geometry information is taken into account in power management decisions by using both Dynamic Programming (DP) and a standard Equivalent Consumption Minimization Strategy (ECMS), derived using Pontryagin minimum principle. Secondly, the contribution of different levels of preview to energy management of plug-in hybrid vehicles (PHEVs) is studied. The gains to fuel economy of plug-in hybrid vehicles with availability of velocity and terrain preview and knowledge of distance to the next charging station are investigated. Access to future driving information is classified into full, partial, or no future information and energy management strategies for real-time implementation with partial future preview are proposed. ECMS as well as Dynamic Programming (DP) is systematically utilized to handle the resulting optimal control problems with different levels of preview. We also study the benefit of future traffic flow information preview in improving the fuel economy of conventional vehicles by predictive control methods. According to the time-scale of the preview information and its importance to the driver, the energy optimization problem is decomposed into different levels. In the microscopic level, a model predictive controller as well as a car following model is employed for predictive adaptive cruise control by stochastically forecasting the driving behavior of the lead car. In the macroscopic level, we propose to incorporate the estimated macroscopic future traffic flow information and optimize the cost-to-go by utilizing a two-dimension Dynamic Programming (2D-DP). The algorithm yields the optimal trip velocity as the reference velocity for the driver or a low level controller to follow. Through the study, we show that energy use and emissions can be reduced considerably by using preview route information. The methodologies discussed in this dissertation provide an alternative mean for the automotive industry to develop more efficient and environmentally friendly vehicles by relying mostly on software and information and with minimal hardware investments

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Energy-Efficient and Semi-automated Truck Platooning

This open access book presents research and evaluation results of the Austrian flagship project “Connecting Austria,” illustrating the wide range of research needs and questions that arise when semi-automated truck platooning is deployed in Austria. The work presented is introduced in the context of work in similar research areas around the world. This interdisciplinary research effort considers aspects of engineering, road-vehicle and infrastructure technologies, traffic management and optimization, traffic safety, and psychology, as well as potential economic effects. The book’s broad perspective means that readers interested in current and state-of-the-art methods and techniques for the realization of semi-automated driving and with either an engineering background or with a less technical background gain a comprehensive picture of this important subject. The contributors address many questions such as: Which maneuvers does a platoon typically have to carry out, and how? How can platoons be integrated seamlessly in the traffic flow without becoming an obstacle to individual road users? What trade-offs between system information (sensors, communication effort, etc.) and efficiency are realistic? How can intersections be passed by a platoon in an intelligent fashion? Consideration of diverse disciplines and highlighting their meaning for semi-automated truck platooning, together with the highlighting of necessary research and evaluation patterns to address such a broad task scientifically, makes Energy-Efficient and Semi-automated Truck Platooning a unique contribution with methods that can be extended and adapted beyond the geographical area of the research reported