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CAREER: Hybrid Estimation and Real-Time Computational Algorithms for Networked Embedded Hybrid Systems


Inseok Hwang


Due to advances in embedded systems and communication technologies, there is an increasing interest in networked embedded hybrid system applications such as transportation systems, networked robotics, sensor network, and biological systems. In the above applications, the typical system consists of a group of subsystems. Each subsystem also has an embedded computer which has unique properties such as interacting physical and logical dynamics and decentralized decision making. Inherent to the networked embedded hybrid system is the presence of heterogeneous uncertainty. Thus, a networked embedded hybrid system is much more complex than a continuous dynamic system on which the current control theory is based. The complexity of a networked embedded hybrid system presents major challenges in the areas of real-time information inference, control, and safety verification. Since the complex behavior of such systems with uncertainties could be modeled as a stochastic hybrid system, the objective of this NSF CAREER research is to develop theory, computational efficient numerical algorithms, and experimental testbeds for stochastic hybrid systems, with application to mobile networked embedded systems (especially air traffic control). To achieve the objective, this project is focusing on the following three topics: real-time hybrid estimation and information inference algorithms and analysis methods are being developed for stochastic hybrid systems. For computational efficiency, algorithms are based on analytical formulation instead of widely-used sample-based estimation algorithms; computationally efficient numerical algorithms based on Differential Transformation are being developed for optimal control of hybrid systems; and the developed algorithms are being validated on experimental test platform (unmanned aerial vehicle (UAV) system).

Project Period: May 1, 2008~ April 30, 2015


Hybrid Systems

Unmanned Vehicles Testbed

Applications: ATC and UAV


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