Hierarchical approximations to optimal control

最优控制的分层近似

• 批准号: 0702221
• 负责人: Emanuel Todorov
• 金额: $ 25.57万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0702221&HistoricalAwards=false
• 关键词: Hierarchical; approximations; optimal; control

Proposal Number: 0702221Proposal Title: Hierarchical approximations to optimal controlPI Name: Todorov, EmanuelPI Institution: University of California-San Diego The objective of this research is develop new algorithms forapproximately-optimal control of complex dynamical systems. The approachcombines inspiration from neuroscience with mathematical advances in controltheory. The algorithms have a hierarchical structure reminiscent of the waythe brain generates complex behavior. The lower level of the hierarchyaugments the body and makes it easier to control. The higher level monitorsprogress and steers the system towards achievement of the common task. Inthis way the complexities due to the body are separated from those due tothe task.Intellectual meritThe project includes two complementary classes of algorithms. The firstclass represents a significant advance in the theory of stochastic optimalcontrol. A general family of problems are identified where the fundamentalequations characterizing the optimal solution turn out to be linear, eventhough the controlled system is nonlinear. The second class of algorithmsrepresents a practical framework for attacking high-dimensional nonlinearproblems, particularly those that arise in biomechanics.Broader impactsThe proposed theoretical developments represent foundational work which islikely to have a lasting impact. The proposed numerical algorithms have thepotential to extend the range of practically-solvable optimal controlproblems. Optimal control is of interest in many fields of science andengineering, including the recovery of motor function via brain-machineinterfaces. Educational activities include mentoring of the graduatestudents funded by this proposal, as well as design and teaching of bothgraduate and undergraduate classes at the interface of Neuroscience andEngineering.

提案号：0702221提案标题：最优控制的层次逼近名称：Todorov， EmanuelPI机构：加州大学圣地亚哥分校本研究的目标是开发复杂动态系统的近似最优控制的新算法。该方法将神经科学的灵感与控制理论的数学进步相结合。这些算法有一个层次结构，让人想起大脑产生复杂行为的方式。层次结构的较低层次增强了主体，使其更易于控制。高层监控进程，并引导系统实现共同任务。这样，身体的复杂性与任务的复杂性就分离了。该项目包括两类互补的算法。第一类代表了随机最优控制理论的重大进展。在一类问题中，尽管被控系统是非线性的，但表征最优解的基本方程却是线性的。第二类算法代表了解决高维非线性问题的实用框架，特别是那些出现在生物力学中的问题。更广泛的影响提出的理论发展代表了基础工作，不太可能产生持久的影响。所提出的数值算法有可能扩展实际可解的最优控制问题的范围。最优控制在许多科学和工程领域都引起了人们的兴趣，包括通过脑机接口恢复运动功能。教育活动包括指导由该提案资助的研究生，以及设计和教学神经科学和工程接口的研究生和本科生课程。