Distributed moving horizon estimation and cooperative predictive control

随着全球竞争的加剧和对经济效益的追求，大型复杂过程在现代制造业中变得越来越普遍。一个大规模的复杂过程是由多个相互关联的单元组成的，这些单元通过物质、能量和信息流紧密结合在一起。现代制造过程的平稳运行在很大程度上依赖于状态估计和过程控制系统。状态估计系统提供对不易测量的过程变量的估计。过程控制系统利用对过程变量的估计，以自动方式在所需操作点驱动或维持过程。传统上，大规模过程的状态估计和过程控制主要是在分散框架内研究的。然而，由于分散框架中完全忽略了子系统之间的相互联系，这可能会导致系统性能恶化，甚至失去闭环系统的稳定性。这一事实导致了开发分布式状态估计和协作控制系统的强烈呼声，这些系统能够在保持分散框架的灵活性的同时实现更好的闭环系统性能。*本研究方案致力于为大规模过程开发高效的分布式状态估计和过程控制方法。这一方案的成功将导致一种有效的分布式状态估计和一种更实用的用于非线性过程的分布式协作预测控制方法，这是目前尚不存在的。这些技术将极大地提高加拿大制造业的经济效益，范围从化工、制药、石化和矿物加工。

Because of increasing global competition and the pursuit of economic profit, large-scale complex processes are becoming more and more common in modern manufacturing industries. A large-scale complex process is composed of multiple interconnected units that are tightly integrated through material, energy and information flows. The smooth operation of modern manufacturing processes highly depends on state estimation and process control systems. A state estimation system provides estimates of process variables that can not be easily measured. A process control system takes advantage of the estimates of process variables to drive or maintain the process at desired operating points in an automatic fashion. ****Traditionally, state estimation and process control of large-scale processes has been primarily studied within the decentralized framework. However, because the interconnections between subsystems are totally neglected in the decentralized framework, it may lead to deteriorated performance or even loss of closed-loop stability. This fact has led to strong calls for the development of distributed state estimation and cooperative control systems which are able to achieve improved closed-loop performance while preserving the flexibility of the decentralized framework.****This research proposal is dedicated to the development of efficient distributed state estimation and process control methods for large-scale processes. The success of this proposal will result in an efficient distributed state estimation and a more practical distributed cooperative predictive control methodology for nonlinear processes that is not available now. These techniques will greatly improve the economic efficiency of Canadian manufacturing industries ranging from chemicals, pharmaceuticals, petrochemicals and mineral processes.