Real time optimisation by extremum seeking control and learning control

通过极值搜索控制和学习控制进行实时优化

• 批准号: FT0991385
• 负责人: Prof Ying Tan
• 金额: $ 48.96万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2010
• 资助国家: 澳大利亚
• 起止时间: 2010-01-04 至 2014-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT0991385
• 关键词: Real; time; optimisation; extremum; seeking

Optimal control technology provides the systematic design of systems that exhibit optimal behaviour, such as maximal productivity, best efficiency, minimal cost and best quality. Real time optimisation finds the solution of the optimal control in real time, relaxing requirements on the system knowledge. The proposed research will build on Australia's well-established strength in control and optimisation, and aim to establish within Australia world-leading expertise in real time optimisation theories and applications. This will have direct benefits to the Australian economy through various engineering applications ranging from vehicle dynamics to emissions reduction to manufacturing process to efficiency improvement of power generation systems.

最佳控制技术提供了具有最佳行为的系统的系统设计，例如最大生产率，最佳效率，最低成本和最佳质量。实时优化可以实时找到最佳控制的解决方案，从而放松了系统知识的要求。拟议的研究将基于澳大利亚在控制和优化方面的实力，并旨在在澳大利亚建立实时优化理论和应用方面的世界领先的专业知识。这将通过从车辆动力学到减少排放再到制造过程再到发电系统的效率来对澳大利亚经济产生直接利益。