Accelerated Finite-time Learning and Control in Cyber-Physical Systems

信息物理系统中的加速有限时间学习和控制

• 批准号: DP230101107
• 负责人: Prof Xinghuo Yu
• 金额: $ 33.19万
• 依托单位: RMIT University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230101107
• 关键词: Accelerated; Finite; time; Learning; Control; Accelerated; Finite; time; Learning; Control

Efficient learning and control in cyber-physical systems such as smart grids and robotic systems are very important for achieving economic and social benefits. This project aims to establish a breakthrough accelerated finite-time dynamics theory and technology to assist in delivering efficient learning and control. Expected outcomes include new distributed accelerated finite-time dynamics based learning and control algorithms and tools for optimal operations in cyber-physical systems. This should provide significant benefits including a practical technology for industry applications in smart grids and robotic systems, and training of the next generation engineers in this technology for Australia.

在智能电网和机器人系统等网络物理系统中的有效学习和控制对于实现经济和社会利益非常重要。该项目旨在建立一个突破性的加速有限时动力学理论和技术，以帮助提供有效的学习和控制。预期的结果包括新的分布式加速有限时动力学的学习和控制算法以及用于网络物理系统中最佳操作的工具。这应该为智能电网和机器人系统中的行业应用提供重要的好处，以及对澳大利亚这项技术的下一代工程师的培训。