A Control-Theoretic Approach to Learning and Approximate Dynamic Programming (ADP) with Applications to High Performance Racing

该项目将使用自动赛车作为新型智能系统设计的测试平台（和教育工具）。该项目将专注于融合计算智能（如强化学习）和控制理论（如混合控制）概念的挑战，以便在多个时间尺度上以集成的方式，自适应地和前瞻性地管理复杂系统。测试平台的目标只是将单圈时间最小化-但希望结果对无人驾驶汽车（UAV）的发展有用。该项目的一个主要目标是建立跨学科的新伙伴关系，如在www.eas.asu.edu/cnnsfadp上描述的国家科学基金会支持的研讨会所讨论的那样，并创建一个支持这些目标的跨学科研究生课程。

This project will use an autonomous racing vehicle as a testbed (and educational tool) for new types of intelligent system design. The project will focus on the challenge of blending concepts from computational intelligence (such as reinforcement learning) and control theory (such as hybrid control) so as to manage a complex system over multiple time scales, in an integrated way, adaptively, and with foresight. The goal in the testbed will simply be to minimize lap time - but it is hoped that the results will be useful to the development of unmanned autonomous vehicles (UAV) in general. A primary goal of the project is to build new partnerships across disciplines, as discussed in the NSF-supported workshop described at www.eas.asu.edu/~nsfadp, and to create a crossdisciplinary graduate course supporting these goals.