Hierarchical Hybrid Control of Multi-Vehicle Systems

多车辆系统可以看作是一个混合系统，表现出离散和连续的行为。由于复杂性和功能性的增加，这些系统变得更难开发。此外，这些系统经常用于安全关键环境中，因此需要高可靠性保证。分布式混合动力系统的设计与实现是一个具有挑战性的课题。本课题研究无人机协同分析与综合的关键技术（分层建模的软件工具、基于优化的混合动力控制）。多车辆系统需要适应手头的任务和来自远程用户的命令。为了便于这类混合动力系统的设计，以下研究重点是解决： （1.）多车辆系统的递阶建模。这个推力的重点是开发一个软件工具包，用于建模分布式多车辆系统。这个工具包允许一个模块化和层次化的方法来编程的审议和反应行为在分布式自治操作。顺序组合、层次组合和并行组合的正式定义允许复杂混合系统的自底向上开发。（2.）分布式混合控制与优化。该推力解决了使用最优控制图和模型预测控制（MPC）的一组车辆的混合控制。在控制层级中确定应该施加MPC的适当级别是重要的。在极端情况下，MPC可以用于整个系统;然而，当车辆的本地控制器已经很好地建立时，这可能不是有利的。在评估这种权衡时，考虑了算法的复杂性，计算时间和稳定性。在此努力下开发的软件和分析工具具有更广泛的影响，因为它们也可以应用于其他领域，如分布式发电厂，运输系统和编队飞行系统。此外，这些方法有可能适用于商业，军事和民用应用，如采矿业，消防和救灾行动，搜索和救援任务，以及国防部的任务，如下一代作战和国土安全。该项目的教育目标是将研究成果整合到本科和研究生课程中，并培养新一代混合和嵌入式系统的学生，这些学生具有这种独特风味的跨学科专业知识。

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.