AIS: Entanglement of Approximate Dynamic Programming and Modern Nonlinear Control for Complex Systems
AIS: Entanglement of Approximate Dynamic Programming and Modern Nonlinear Control for Complex Systems
批准号:
1101401
负责人:
Zhong-Ping Jiang
金额:
$28.18万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2015-07-31
中文摘要
本研究的目的是为鲁棒自适应/近似动态规划开发一个新的框架,以解决工程和生物学中出现的重大挑战,如智能电网、大脑研究、机器人和飞行控制。该方法是利用强化学习系统和神经网络以及现代非线性控制这两个活跃研究领域的通用技术的明显优势。这一跨学科的研究倡议是由构建类脑强化学习系统和最终理解大脑功能的需求驱动的,在不同方面都具有重要意义。它将极大地推动近似动态规划的发展,并解决真正的无模型情况。此外,对于工程和生物学引起的当代复杂问题,建立精确的数学模型通常是非常困难的,如果不是不可能的话,这个提议采用了一种基于PI?我们研究的是非线性小增益理论。更广泛的影响建议的工作将导致在相互关联的复杂系统中开发健壮的自适应批评设计的新工具。这些工具不仅有望在智能电网、机器人和飞行控制等新兴工程应用中找到应用,而且还将有助于深入了解大脑功能和构建类脑强化学习工程系统的长期目标。拟议的研究将通过吸引来自多个领域和部门的学生,对PI所在机构的教育产生实质性的直接影响。
英文摘要
The objective of this research is to develop a new framework for robust adaptive/approximate dynamic programming to address grand challenges arising from engineering and biology, such as smart grid, brain research, robotics, and flight control. The approach is to take explicit advantages of versatile techniques from two active areas of research in reinforcement learning systems and neural networks and in modern nonlinear control.Intellectual MeritThis interdisciplinary research initiative, driven by the need in building brain-like reinforcement learning systems and in understanding ultimately the brain function, is significant in different aspects. It will significantly advance the state of the art on approximate dynamic programming and address the truly model-free situation. In addition, instead of building exact mathematical models, which often is very hard, if not impossible, for contemporary complex problems arising from engineering and biology, this proposal adopts a novel interconnected system viewpoint on the basis of the PI?s work on nonlinear small-gain theory. Broader ImpactsThe proposed work will lead to the development of new tools for robust adaptive critic designs in interconnected complex systems. Not only these tools are expected to find applications in emerging engineering applications such as smart grid, robotics and flight control, but also they will help gain a deeper insight toward the long-term goal in understanding brain functions and building brain-like reinforcement learning engineering systems. The proposed research will have a substantial direct impact upon education at the PI's institution by engaging students from several areas and departments.
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