CAREER: Real-time Convex Optimization for High-Performance Control of Autonomous Systems

职业：自治系统高性能控制的实时凸优化

• 批准号: 1454543
• 负责人: Behcet Acikmese
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454543&HistoricalAwards=false
• 关键词: CAREER; Real; time; Convex; Optimization

This Faculty Early Career Development (CAREER) Program grant will build innovative mathematical and computational methods of decision making for control of next generation high-performance autonomous systems. A broad range of emerging technologies utilize autonomy, including self-driving vehicles, quadrotors for delivery, search and rescue robots, mobile sensors for environmental monitoring, climate control systems for warehouses and buildings, automated drug dispensing, and next-generation power networks. Large-scale deployment of autonomous systems is a critical technological leap that will fundamentally transform our lives. Autonomous systems will maximize efficiency and reliability. Furthermore, they will improve safety by reducing or removing direct human involvement in hazardous tasks. However, autonomous systems present fundamentally difficult technical challenges to go beyond the limitations of the current state of the art in automation. This project lays the mathematical and computational foundations for a transformative approach to autonomy, one that makes previously impractical calculations tractable, and provides guaranteed levels of performance and stability. To meet fundamentally new and distinctly challenging performance and reliability requirements, future autonomous systems must make best possible decisions to control their actions without a human operator in the loop. They must utilize their full performance envelopes, while simultaneously satisfying critical mission and environmental constraints. Consequently, mathematical optimization problems are ubiquitous in autonomous control. Though optimization provides a powerful formulation framework, thus far real-time optimization based control has not transitioned to common practice due to shortcomings in the current algorithmic capabilities. To enable real-time optimization based control, this CAREER grant will develop a comprehensive theory of convexification -- formulation of control problems as tractable convex optimization problems -- enabling high performance control that is currently not achievable in many applications. Specifically, it will develop: accurate formulations of autonomous control problems as convex optimizations; customized numerical optimization algorithms that exploit problem structure for extremely fast and reliable onboard computations; and rigorous verification methods to certify the performance and robustness of the resulting autonomous control algorithms. These advances and resulting insights will also benefit systematic systems engineering and design for autonomous systems, high-level autonomous mission planning, and control of autonomous multi-agent systems. Collectively the theoretical and algorithmic products will form a strong technical foundation allowing the transition of real-time optimization-based control for autonomous systems into practice.

该教师早期职业发展（CAREER）计划拨款将建立创新的数学和计算决策方法，以控制下一代高性能自主系统。一系列新兴技术都利用了自主性，包括自动驾驶车辆、用于送货的四旋翼飞行器、搜索和救援机器人、用于环境监测的移动传感器、仓库和建筑物的气候控制系统、自动配药和下一代电力网络。大规模部署自主系统是一次关键的技术飞跃，将从根本上改变我们的生活。自主系统将最大限度地提高效率和可靠性。此外，它们将通过减少或消除人类直接参与危险任务来提高安全性。然而，自主系统提出了根本上困难的技术挑战，以超越当前自动化技术水平的限制。该项目为一种变革性的自主方法奠定了数学和计算基础，使以前不切实际的计算变得易于处理，并提供有保证的性能和稳定性水平。为了满足全新且极具挑战性的性能和可靠性要求，未来的自主系统必须做出最佳决策来控制其行为，而无需人工操作员参与。他们必须充分利用其性能范围，同时满足关键任务和环境限制。因此，数学优化问题在自主控制中普遍存在。尽管优化提供了强大的公式框架，但迄今为止，由于当前算法能力的缺陷，基于实时优化的控制尚未转变为常见实践。为了实现基于实时优化的控制，这项职业资助将开发一种全面的凸化理论——将控制问题表述为可处理的凸优化问题——实现目前在许多应用中无法实现的高性能控制。具体来说，它将开发： 将自主控制问题准确地表述为凸优化；定制的数值优化算法，利用问题结构进行极其快速和可靠的机载计算；以及严格的验证方法，以证明所得自主控制算法的性能和鲁棒性。这些进步和由此产生的见解也将有利于自主系统的系统系统工程和设计、高级自主任务规划以及自主多智能体系统的控制。总的来说，理论和算法产品将形成强大的技术基础，使基于实时优化的自主系统控制能够转化为实践。