Distributed moving horizon estimation and cooperative predictive control

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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.