AIS: Entanglement of Approximate Dynamic Programming and Modern Nonlinear Control for Complex Systems

这项研究的目标是开发一个新的稳健自适应/近似动态规划框架，以应对来自工程和生物学的重大挑战，如智能电网、脑研究、机器人和飞行控制。这种方法是从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的研究领域中利用多种技术的明显优势。智力价值这种跨学科的研究倡议，在建立类脑强化学习系统和最终理解大脑功能的需要的推动下，在不同的方面具有重要意义。它将极大地提高近似动态规划的技术水平，并解决真正无模型的情况。此外，对于当代工程和生物学中出现的复杂问题，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，该建议采用了一种新的互联系统观点，该观点基于皮耶？S关于非线性小增益理论的工作。更广泛的影响拟议的工作将导致开发新的工具，用于在相互关联的复杂系统中进行稳健的自适应批评设计。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，而且还将有助于更深入地了解大脑功能和构建类似大脑的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几个领域和部门的学生，对该机构的教育产生重大的直接影响。

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.