ADAPTIVE CRITIC DESIGN NEUROCONTROLLERS FOR NONLINEAR LARGE SCALE SYSTEMS AND ADVANCED NETWORK MANAGEMENT

用于非线性大规模系统和高级网络管理的自适应批评设计神经控制器

• 批准号: 0080764
• 负责人: Donald Wunsch
• 金额: $ 42.96万
• 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: 2000
• 资助国家: 美国
• 起止时间: 2000-10-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0080764&HistoricalAwards=false
• 关键词: ADAPTIVE; CRITIC; DESIGN; NEUROCONTROLLERS; NONLINEAR

0080764WunschNonlinear non-stationary large-scale systems such as electric power networks, telecommunication networks, financial and transportation systems are difficult to control and manage. Mathematical models of such systems are typically derived based on linear techniques, and wide margins of safety ensure stable operation. Advanced identification and control schemes are necessary in order to improve the throughput-versus-reliability tradeoff of such systems.Preliminary work by these PIs has shown that advanced, value-based control methods can substantially improve the ability of individual turbogenerators to tolerate unexpected system disturbances. This project will extend that work to distributed networks of power generators and power grids in general. The underlying technology is an advanced form of learning-based Approximate Dynamic Programming or "Reinforcement Learning" which outputs measures of value for individual variables, or dynamic "shadow prices," rather than the conventional measures of global strategic utility. Success in this area may also have benefits for other domains where distributed control and distributed intelligence are needed.

0080764Wunsch非线性非平稳大规模系统，如电力网络，电信网络，金融和交通系统是难以控制和管理。 此类系统的数学模型通常基于线性技术导出，并且宽的安全裕度确保稳定的操作。 先进的识别和控制方案是必要的，以提高吞吐量与可靠性权衡这些systems.Preliminary工作PI已经表明，先进的，基于值的控制方法可以大大提高个别汽轮发电机的能力，以容忍意外的系统干扰。 该项目将把这项工作扩展到一般的发电机和电网的分布式网络。 其基础技术是一种基于学习的近似动态规划或“强化学习”的高级形式，它输出单个变量的价值度量，或动态“影子价格”，而不是传统的全球战略效用度量。 这一领域的成功也可能对其他需要分布式控制和分布式智能的领域有好处。