Cooperation-based optimization of industrial supply chains

Abstract:

This talk provides an overview of cooperative distributed model predictive control and presents current challenges in applying this approach to supply chain models. We first provide a brief introduction and comparison of four model predictive control approaches: decentralized, noncooperative, cooperative, and centralized. Next, a theory for cooperative MPC with linear models is presented. This theory is applicable to any finite number of subsystems with the following features: hard input constraints are satisfied; the distributed control provides nominal stability for the same set of plants as centralized control; terminating the iteration of the distributed controllers prior to convergence retains closed-loop stability; in the limit of iterating to convergence, the control feedback is plantwide Pareto optimal and equivalent to centralized control; no coordination layer is employed. Stability analysis of cooperative MPC is addressed by showing it to be a special case of suboptimal MPC. Given a distributed stabilizability assumption, cooperative MPC is shown to be exponentially stabilizing. Then, given a detectability assumption, we establish that under perturbation from a stable state estimator, the origin remains exponentially stable.

Both the stabilizability and detectability assumptions fail in the cases of interest to supply chain optimization. The talk concludes by presenting various options to modify the cooperative MPC approach to handle supply chain models.

Slides

Biography:James B. Rawlings received the B.S. from the University of Texas in 1979 and the Ph.D. from the University of Wisconsin in 1985, both in Chemical Engineering. He spent one year at the University of Stuttgart as a NATO postdoctoral fellow and then joined the faculty at the University of Texas. He moved to the University of Wisconsin in 1995 and is currently the Paul A. Elfers Professor of Chemical and Biological Engineering and the co-director of the Texas-Wisconsin-California Control Consortium (TWCCC).

His research interests are in the areas of chemical process modeling, molecular-scale chemical reaction engineering, monitoring and control, nonlinear model predictive control and moving horizon state estimation.