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

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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.