Hierarchical Hybrid Control of Multi-Vehicle Systems

Oklahoma State University, Stillwater OK

Investigators

Abstract

Multi-vehicle systems can be viewed as hybrid systems that exhibit both discrete and continuous behaviors. These systems become harder to develop due to increasing complexity and functionality. Furthermore, these systems are frequently used in safety critical environments, and thus, require high assurance in reliability. The design and implementation of distributed hybrid systems remains a challenging task. This project investigates key technologies (software tools for hierarchical modeling, and optimization-based hybrid control) for analysis and synthesis of cooperative unmanned vehicles. Multi-vehicle systems need to adapt to the task in hand and commands from a remote user. To facilitate the design of this class of hybrid systems, the following research thrusts are addressed: (1.) Hierarchical modeling of multi-vehicle systems. This thrust focuses on developing a software toolkit for modeling distributed multi-vehicle systems. This toolkit allows a modular and hierarchical approach to programming deliberative and reactive behaviors in distributed autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex hybrid systems. (2.)Distributed hybrid control and optimization. This thrust addresses hybrid control of a team of vehicles using optimal control graphs and model predictive control (MPC). Determining the proper level in the control hierarchy at which MPC should be imposed is important. In the extreme, MPC can be used for the entire system; however this may not be advantageous when local controllers for vehicles have already been well established. In evaluating this trade-off, the complexity of the algorithm, computation time, and stability are considered. The software and analysis tools developed under this effort have a broader impact, as they can also be applied to other domains such as distributed power plants, transportation systems, and formation flight systems. Furthermore, these methodologies have the potential to be applicable for commercial, military and civilian applications like mining industry, firefighting and disaster relief operations, search and rescue missions, and DoD missions such as next generation battle warfare, and homeland security. The educational goals of this project are to integrate the research findings into undergraduate and graduate courses and to train a new generation of students on hybrid and embedded systems with cross disciplinary expertise of this unique flavor.