权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Learning-based Adaptive Optimal Control Principles for Human Movements

基于学习的人体运动自适应最优控制原理

基本信息

批准号：
1903781
负责人：
Zhong-Ping Jiang
金额：
$ 29.36万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903781&HistoricalAwards=false
关键词：
Learning based Adaptive Optimal Control

项目摘要

The study of human movement, driven by the desire to expand our understanding of the brain, holds great promise for devising new therapies for people who suffer from neurodegenerative disorders such as Parkinson's disease and Huntington's disease. This proposal aims to deepen our preliminary research in learning-based control theory as a new computational principle of sensorimotor control, while, at the same time, validating the proposed theory through numerical simulations and biological experiments. It is an interdisciplinary project that combines tools and methods from reinforcement learning, nonlinear control theory, and adaptive dynamic programming. Rigorous stability and robustness analysis as well as convergence proofs for the proposed learning algorithms will be carried out. A fundamentally novel aspect of the proposal is that attention will be paid to continuous-time dynamical systems described by differential equations as opposed to the conventional models used in the past literature such as discrete-time systems and Markov decision processes (MDPs). Intellectual merit:This interdisciplinary project is aimed at developing and validating robust adaptive dynamic programming as a theory of human sensorimotor learning and control. To this end, there is a great need to develop new results that go beyond the present literature by considering a wider class of continuous-time stochastic systems with both additive and multiplicative noise and strong nonlinearities. Human behavioural experiments are to be designed to test and develop computational models of sensorimotor control. Broader impacts:Even though human movement problems are targeted in this project, it is expected that the findings of this project toward learning-based control theory will be useful for other problems arising from engineering and computational neuroscience such as robotic rehabilitation. The proposal also includes a plan of educational activities centered on student supervision, curriculum development, knowledge dissemination, and collaboration with New York University Center for Neural Science. The PI hopes to organize workshops and invited sessions at major conferences, providing an opportunity to students and junior researchers to interact with leading authorities in automatic control and computational neuroscience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对人类运动的研究，是为了扩大我们对大脑的理解，这对为患有帕金森病和亨廷顿病等神经退行性疾病的人设计新的治疗方法大有希望。该提案旨在深化我们对基于学习的控制理论作为感觉运动控制的新计算原理的初步研究，同时通过数值模拟和生物实验验证所提出的理论。它是一个跨学科的项目，结合了强化学习，非线性控制理论和自适应动态规划的工具和方法。严格的稳定性和鲁棒性分析以及收敛性证明所提出的学习算法将进行。一个从根本上新颖的方面的建议是，将注意力集中到连续时间动态系统描述的微分方程，而不是传统的模型在过去的文献中使用，如离散时间系统和马尔可夫决策过程（MDP）。智力价值：这个跨学科的项目旨在开发和验证强大的自适应动态规划作为人类感觉运动学习和控制的理论。为此，有很大的必要发展新的结果，超越目前的文献，考虑更广泛的一类连续时间随机系统的加性和乘性噪声和强非线性。人类行为实验的目的是测试和开发感觉运动控制的计算模型。更广泛的影响：尽管该项目针对的是人类运动问题，但预计该项目对基于学习的控制理论的研究结果将有助于解决工程和计算神经科学产生的其他问题，如机器人康复。该提案还包括一项以学生监督、课程开发、知识传播以及与纽约大学神经科学中心合作为中心的教育活动计划。PI希望在主要会议上组织研讨会和特邀会议，为学生和初级研究人员提供与自动控制和计算神经科学领域领先权威互动的机会。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。