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

基本信息

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

项目摘要

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.

摘要这项研究的总体目标是为在线强大的自适应动态编程（ADP）提供基于严格的设计和数学框架的支持，并在不利用策略和价值迭代的情况下为未知的线性和非线性网络控制系统（NCS）提供保证的最佳控制器。这里采用的方法采用自适应网络学习作为基本障碍，并利用了过去的成本到访问信息的历史，并在不使用系统模型和离线学习阶段的情况下更新了控制输入的信息，而无需使用系统模型和离线学习阶段。Intlectualfurettual提出的拟议研究提供了一个更强大而强大的范式的精心范围的范围，即精通学习的范围。提出的努力将推动ADP中的最新状态以进行控制，并在不仅具有不确定的系统动态和干扰的情况下确保稳定性和性能，还可以在不使用迭代方法的情况下进行网络缺陷，例如随机延迟，数据包丢失和量化错误。更广泛的影响将直接影响所有实时实用系统，例如智能电网的有效操作和能源安全，近发射汽车控制系统和下一代制造系统。这种控制方案是美国行业全球竞争力所必需的。技术转移将通过NSF行业/大学合作研究中心进行智能维护系统。在研究界，这项工作将激发更多理论上的结果，同时为下一代学生，未来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。