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

本研究的目标是开发一个新的框架，强大的自适应/近似动态规划，以解决工程和生物学，如智能电网，大脑研究，机器人和飞行控制所带来的巨大挑战。该方法是采取明确的优势，从两个活跃的研究领域，在强化学习系统和神经网络和现代非线性控制的多功能技术。智力MeritThis跨学科的研究倡议，需要在建设类脑强化学习系统，并最终在理解大脑的功能，是在不同的方面显着。它将大大推进近似动态规划的最新技术水平，并解决真正的无模型情况。此外，而不是建立精确的数学模型，这往往是非常困难的，如果不是不可能的，为当代复杂的问题所产生的工程和生物学，该建议采用了一种新的互联系统的观点的基础上PI？他在非线性小增益理论方面的工作。更广泛的影响拟议的工作将导致强大的自适应批评设计在互联复杂系统的新工具的发展。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.