Adaptive Dynamic Programming-based Control of Unknown Networked Control Systems

基于自适应动态规划的未知网络控制系统控制

• 批准号: 1128281
• 负责人: Jagannathan Sarangapani
• 金额: $ 34.61万
• 依托单位: Missouri University of Science and Technology
• 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
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1128281&HistoricalAwards=false
• 关键词: Adaptive; Dynamic; Programming; based; Control

AbstractThe overall objective of this study is to provide online robust adaptive dynamic programming (ADP) based optimal controllers with guaranteed performance, supported by a rigorous design and mathematical framework, and without utilizing policy and value iterations, for unknown linear and nonlinear networked control systems (NCS). The approach taken here employs adaptive network learning as a fundamental block and utilizes past history of cost-to-go information, and updates the control input once a sampling interval in a forward-in-time manner without using a system model and offline learning phase for the NNs.Intellectual MeritThe proposed research presents an opportunity to deal with a more powerful and unified paradigm of complex learning problems and envisions a brain-like controller. The proposed effort will advance the state of the art in ADP for control and guarantees stability and performance in the presence of not only uncertain system dynamics and disturbances, but also network imperfections such as random delays, packet losses and quantization errors without using iterative approach. Broader ImpactThis effort would directly impact all real-time practical systems such as the efficient operation and energy security of the smart grid, near zero-emission automotive control systems, and next generation manufacturing system. Such control schemes are required for global competitiveness of the US industry. Technology transfer will occur through the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems. Within the research community, this work will inspire more theoretical results while providing training opportunities to next generation students, future scientists and engineers including from underrepresented groups.

本文的总体目标是为未知的线性和非线性网络控制系统（NCS）提供在线鲁棒自适应动态规划（ADP）最优控制器，该控制器由严格的设计和数学框架支持，不需要策略和数值迭代。这里采用的方法采用自适应网络学习作为基本块，并利用过去的成本历史信息，和更新控制输入一次采样间隔在一个向前的时间方式，而不使用系统模型和离线学习阶段的神经网络。智力MeritThe拟议的研究提出了一个机会，以处理一个更强大的和统一的范式复杂的学习问题，并设想一个大脑-就像控制器。所提出的努力将推进最先进的ADP控制和保证稳定性和性能的存在下，不仅不确定的系统动态和干扰，但也网络的不完善，如随机延迟，丢包和量化误差，而不使用迭代方法。 更广泛的影响这一努力将直接影响所有实时实用系统，如智能电网的高效运行和能源安全，近零排放汽车控制系统和下一代制造系统。这种控制计划是美国工业在全球竞争力所必需的。技术转让将通过NSF工业/大学智能维护系统合作研究中心进行。在研究界，这项工作将激发更多的理论成果，同时为下一代学生，未来的科学家和工程师提供培训机会，包括来自代表性不足的群体。