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

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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 networks, and biological systems. In the above applications, the typical system consists of a group of subsystems. Each subsystem also has an embedded computer and possesses 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 most 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, computationally efficient numerical algorithms, and experimental testbeds for stochastic hybrid systems, with application to mobile networked embedded systems (emphasizing air traffic control). To achieve the objective, this project is focusing on the following topics: real-time hybrid estimation and information inference algorithms and analysis methods 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 for reachable set computation. The developed algorithms are being validated on experimental test platforms, including autonomous air vehicles.