Adaptive Dynamic Programming for Continuous-Time Systems and Networked Agents on Graphs

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

Proposal Number: ECCS-0801330 Proposal Title: Adaptive Dynamic Programming for Continuous-Time Systems and Networked Agents on Graphs PI Name: Lewis, Frank L. PI Institution: University of Texas at Arlington Objective and Approach The objective of this research is to improve the performance of human engineered systems by designing better high performance automatic feedback control structures using techniques from the Computational Intelligence field. The approach is to use adaptive dynamic programming (ADP) methods for systems working in continuous-time, a technique not yet fully developed or exploited for control systems design. The PI?s prior work shows that ADP for continuous-time systems provides a novel framework for high-performance control system design from which emerges a multi-timescale separation of control functionality mirroring natural multi-timescale structures in the human brain. Intellectual Merit. Regulation systems in biological structures are both optimal and adaptive in real-time, allowing on-line learning to achieve the best performance. However, manmade control systems are either optimal off-line designs or real-time adaptive methods. This research will use ADP to develop unified automatic control design methods that are both optimal and adaptive in real-time. Mathematically rigorous proofs of performance will be provided. Broader Impacts. This work will develop better feedback control structures that will facilitate better performance for aircraft, engines, industrial processes, etc. It will contribute to theoretical foundations bringing together the Computational Intelligence and Control Systems communities. Comprehensive comparisons between ADP-based techniques and standard adaptive & robust control techniques will be made through implementations on electro-mechanical laboratory equipment and on a three-area power network testbed. Development of cooperative ADP algorithms for systems on communication graphs will allow more effective control of networks of electric power systems and vehicle formations. Ongoing activities in research curriculum development will facilitate transferring research results to education and training at UTA. This grant will allow extension of existing Automation and Robotics Research Institute (ARRI) programs supporting women in engineering, US undergraduate student research, and K-12 outreach. The award-winning SBIR program will facilitate technology transfer to US industry.