Composite fast‐slow MPC design for nonlinear singularly perturbed systems |
| |
| Authors: | Xianzhong Chen Mohsen Heidarinejad Jinfeng Liu Panagiotis D Christofides |
| |
| Affiliation: | 1. Dept. of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095;2. Dept. of Electrical Engineering, University of California, Los Angeles, CA 90095 |
| |
| Abstract: | The design of a composite control system for nonlinear singularly perturbed systems using model predictive control (MPC) is described. Specifically, a composite control system comprised of a “fast” MPC acting to regulate the fast dynamics and a “slow” MPC acting to regulate the slow dynamics is designed. The composite MPC system uses multirate sampling of the plant state measurements, i.e., fast sampling of the fast state variables is used in the fast MPC and slow‐sampling of the slow state variables is used in the slow MPC. Using singular perturbation theory, the stability and optimality of the closed‐loop nonlinear singularly perturbed system are analyzed. A chemical process example which exhibits two‐time‐scale behavior is used to demonstrate the structure and implementation of the proposed fast–slow MPC architecture in a practical setting. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1802–1811, 2012 |
| |
| Keywords: | process control optimization simulations process dynamics |
|
|
