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Dynamics and Control Research

D & C Lab Facilities
D. Andrisani
Professor Andrisani presently has two major funded research areas. The first area, precision location of ground targets from airborne platforms, is funded by the National Imagery and Mapping Agency (NIMA) and the US Air Force X-45A Program Office at the Wright Aeronautical Laboratory (through an umbrella contract from Rosettex (a.k.a. SRI International). The second research area, air traffic management, is funded by Metron Aviation, Inc. and the e-Enterprise Center at Discovery Park. Professor Andrisani is currently funded from Metron Aviation Inc. to study the automatic determination of the intent of a commercial airline pilot flying in controlled airspace. This funding area, called "intent inference," represents a major new research thrust into the air traffic management area. Leveraging in part off this work, Professor Andrisani and Professors Mario Rotea, Thomas Carney, Michael Nolan and Alok Chaturvedi recently were awarded funding from Discovery Park to perform "Air Traffic Management Research for the 21st Century and Beyond."
M. J. Corless
Most of the research is concerned with obtaining tools which are useful in the analysis and control of systems containing significant uncertainty. These uncertainties are characterized deterministically, rather than stochastically. The systems treated can be linear or nonlinear and continuous-time or discrete-time. The major application of the research is in the analysis and control of aerospace and mechanical systems. In these applications, some of the research focuses on the effect of flexible elements.
D. DeLaurentis
Dr. DeLaurentis' doctoral research was motivated by the emerging paradigm of Integrated Product and Process Development (IPPD), which sought to bring historically process-based statistical approaches to product design. His work focused upon the characterization, representation, and quantification of the impact of uncertainty on aerospace design problems. Unique techniques were developed to accomplish this, including the use of Design of Experiments combined with probability theory to obtain rapid estimates of the cumulative distribution functions (CDFs) of key system metrics and objectives. Subsequently, a more direct adoption of robust feedback control concepts has been applied resulting in a new formulation for the design problem to include technology impact assessments and requirement exploration. In addition to methodological advances, the type of problems being faced by the community is changing significantly. Aerospace problems are increasingly becoming recognized as of the "system-of-systems" type, consisting of multiple interacting complex systems of differing type and function. The air transportation system is one example, as are associated issues such as personal air transportation and airborne delivery logistics architectures. Such challenges will require more than just "vehicle synthesis"; they will require "system-of-systems synthesis". Thus, Dr. DeLaurentis has embarked on two projects exploring the use of system dynamics, agent-based modeling, and other approaches to address these multi-disciplinary, multi-system problems. In addition, the areas of network theory and complexity are being explored, to better account for interconnections as well as nodes (i.e. systems) in designing future system-of-systems. The ultimate goal is to better define individual system requirements by understanding the context in which they will operate and to ensure that such operation will be robust over a wide range of plausible futures.
A. E. Frazho
This research develops and applies operator theory to problems in deterministic and stochastic control systems. These techniques are used to design models for both linear and nonlinear control systems. We also obtain fast recursive algorithms for computing reduced order models. This also yields a theory of Hinfinity controller reduction and pole placement with applications to large space structure control. Finally, these techniques are used to solve problems in signal processing and inverse scattering theory.
I. Hwang
Dr. Hwang's research has been strongly motivated by difficult and interesting practical problems such as controlling multiple-vehicle systems. Controlling multiple-vehicle systems is one of the most important and challenging aspects of modern system theory and practice. Control of such systems involves the analysis of multiple dynamical systems which have inherently decentralized structures. The motions of vehicles have to be coordinated in such a way that the vehicles achieve their goals without conflicts between them. This requires path planning (computing optimal trajectories of vehicles from starting positions to destinations) and conflict detection and resolution. Path planning and conflict detection and resolution require information about individual vehicles, and therefore communication between vehicles for sharing this information is important. Multiple-vehicle systems encompass a variety of applications, including groups of mobile robots (e.g., UAS), ad-hoc sensor networks, and air traffic control. In addition, Dr. Hwang's research interests include control theory for hybrid and nonlinear systems, Unmanned Aircraft Systems, safety and security of Cyber-Physical Systems (e.g., aircraft, spacecraft, Air Traffic Control systems, and UAS), and space applications.
D. Sun
My research areas include air traffic control, strategic traffic flow management, dynamic airspace configuration, and studies for Next Generation Air Transportation System (NextGen). I am interested in mathematical modeling and control of physical networked dynamical systems. Constraints are inherent to physical problems, yet they often make mathematical formulations hard to solve. I am interested in developing algorithms to solve these constrained optimization problems, in particular, algorithms to solve large scale problems that were beyond the reach of standard techniques. With an emphasis on aerospace engineering, I am interested in advancing the state of science in modeling and solution of stochastic, dynamic air traffic flow management problems, helping define and build the NextGen traffic flow management vision, philosophy, and architecture. I am also interested in implementing my algorithms on real-time platforms and building toolkits (software) for decision support. For air traffic control, the long term goal of the toolkit is the partial automation of some functions currently performed by humans, with the objective of improving safety and performance while easing workload.