模糊动态系统HPPD型控制方法研究尚处于初步阶段，已有理论方法与实际工业应用需求之间缺少必要的适配，理论成果远不能满足工业生产中实际控制任务的相关需求。本项目聚焦于具有自主适配功能的模糊动态系统HPPD型控制设计问题，克服已有方法自身缺陷且充分考虑实际应用需求，以获得能够满足工业生产中的实际控制任务需求的创新性理论方法为目标，开展如何解决因片面追求降低保守性而忽略了冗余变量引入造成的离线计算负担骤增问题、满足工业控制计算机在线计算能力限制的HPPD型模糊控制器设计问题、考虑工业环境下的典型物理约束和功能需求的多目标多因素协同设计问题等方面的研究；最后，还将结合申请人较为熟悉擅长的冷轧带钢板形控制系统，将理论研究成果在实际工业控制系统中进行应用验证与完善。本项目研究工作的顺利实施将进一步丰富和发展基于HPPD技术的智能模糊控制理论，并为有效解决实际工业背景下的智能控制问题提供更多的理论支持。

The research on homogenous polynomially parameter-dependent(HPPD)-type control synthesis of fuzzy dynamical systems is still in the primary stage, and there is no necessary adaptation between existing theoretical methods and practical industrial application requirements. Therefore, available theoretical achievements cannot meet the demand of actual control tasks in industrial production. With the purpose of achieving some innovative theoretical approaches that meet the demand of actual control tasks in industrial production, this project focuses on HPPD-type control synthesis of fuzzy dynamical systems with autonomous adaptation function by means of overcoming those defects of existing methods and considering practical industrial application requirements. In details, several key scientific problems will be solved in the project, i.e., the problem of reducing unbearable off-line computational burden that has been brought by introducing too many redundant variables, the problem of designing HPPD-type fuzzy controller that meets the demand of on-line computational ability of industrial control computers, and the problem of fuzzy co-design in a multi-object and multi-factor environment where those typical physical constraints and functional requirements are fully considered. Finally, these obtained theoretical methods will be applied and consummated in real industrial control system, such as the flatness control system for cold rolled strip. It is expected that this research could enrich and develop the intelligent fuzzy control theory based on HPPD techniques, and also provide theoretical foundation for solving actual industrial control problems via intelligent methods.