James B. Rawlings Research Group

Aswin Venkat
B.Tech-M.Tech Chemical Engineering, Indian Institute of Technology, Bombay
Ph.D., University of Wisconsin-Madison, 2005

Current Work

Current Work

Communication based Decentralized MPC

Model Predictive Control(MPC) is one of the leading advanced control technologies employed today in the process industries. The ability to incorporate complex objectives as well as constraints in a unified framework make it an extremely attractive and handy tool. MPC technology has evolved tremendously over the years. However most advancements in MPC are geared towards its implementation in a single processing unit. Any complex system, a chemical plant for instance, is comprised of a number of subsystems. These subsystems are invariably linked through shared resources, material and energy flows and/or information links. This interaction complicates controller design. In the traditional unit operations approach to system design, each subsystem is constructed with its individual control system forming a pure decentralized control setup. Decentralized control assumes that each subsystem functions independently and is not influenced by the subsystems around it. Since this assumption is not valid, it would be reasonable to assume that control system performance could be improved if some information about the nature of the interactions between the subsystems could be used in making control decisions. Our research is aimed at utilizing the inherent characteristics of the MPC scheme; modifying and gearing these towards obtaining improved plantwide control. However in this process, we also strive to minimize structural changes in the current industrial control configurations thereby enhancing the possibility of swift implementation.

Communication based Decentralized MPC

First Level of Information Exchange

As a first step towards enhancing plantwide control, we employ the concept of information-exchange between the various model predictive controllers through various levels. Communication constitutes the most basic level of information exchange, each controller receives information about the most likely course of action for the set of subsystems that influence it. This information is then used by the regulator to generate a suitable control move. The information exchange is made possible by the introduction of an additional interface viz. the Communicator. The communicator is akin to a supervisory block for the set of controllers. It receives information from each of the individual controllers, processes it and sends back relevant information that pertains to the effect of the various subsystems on the subsystem under consideration. The various controllers then generate control moves taking this additional information into account.


Aswin N. Venkat.
Distributed Model Predictive Control: Theory and Applications.
PhD thesis, University of Wisconsin-Madison, October 2006.

Aswin N. Venkat, James B. Rawlings, and Stephen J. Wright.
A framework for integrating model predictive controllers to control large-scale systems.
Annual AIChE Meeting, Cincinnati, OH, October 2005.

Aswin N. Venkat, James B. Rawlings, and Stephen J. Wright.
Stability and optimality of distributed model predictive control.
CDC-ECC Joint Conference, Seville, Spain, 2005.

Aswin N. Venkat, James B. Rawlings, and Stephen J. Wright.
Plant-wide optimal control with decentralized MPC.
In DYCOPS, Boston, MA, July 2004.

Ravindra D. Gudi, James B. Rawlings, Aswin Venkat, and N. Jabbar.
Identification for decentralized MPC.
In DYCOPS, Boston, MA, July 2004.

Personal Web Page: jbrwww.che.wisc.edu/~venkat

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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706