Adaptive Dynamic Programming-based Control of Unknown Networked Control Systems

摘要本研究的总体目标是为未知线性和非线性网络控制系统（NCS）提供基于在线鲁棒自适应动态规划（ADP）的性能保证最优控制器，该控制器由严格的设计和数学框架支持，无需使用策略和值迭代。本文采用的方法采用自适应网络学习作为基本模块，并利用过去的成本信息历史，并且在不使用系统模型和网络离线学习阶段的情况下，以向前及时的方式更新一次采样间隔的控制输入。提出的研究提供了一个处理复杂学习问题的更强大和统一范式的机会，并设想了一个类似大脑的控制器。所提出的努力将推进ADP控制的最新技术，并保证在不确定的系统动力学和干扰存在的情况下的稳定性和性能，而且在不使用迭代方法的情况下，还可以保证随机延迟、数据包丢失和量化误差等网络缺陷的存在。更广泛的影响：这项工作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、接近零排放的汽车控制系统和下一代制造系统。这种控制方案是美国工业全球竞争力所必需的。技术转让将通过美国国家科学基金会工业/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。

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.