A computationally efficient nonlinear MPC algorithm

In this paper, a novel model predictive control (MPC) algorithm for control of nonlinear multivariable systems is proposed. The online computational demand of the algorithm depends only on the number of manipulated variables; it does not depend on the input (or control) horizon. Thus, the online computational demand is significantly smaller than conventional nonlinear model predictive control algorithms which attempt to solve the online optimization problems exactly. We show that asymptotic stability can be guaranteed in some cases. Its feasibility for practical implementation is demonstrated on a distillation column dual composition control problem using a rigorous tray-by-tray model (with input horizon of 10).

[1]  K Veit,et al.  The reluctant entrepreneur. , 1992, Harvard business review.

[2]  G. Gattu,et al.  Nonlinear Quadratic Dynamic Matrix Control with State Estimation , 1992 .

[3]  Lorenz T. Biegler,et al.  Optimization approaches to nonlinear model predictive control , 1991 .

[4]  Frank Allgöwer,et al.  A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability , 1997, 1997 European Control Conference (ECC).

[5]  Dag Ljungquist,et al.  Predictive Control based upon State Space Models , 1988, 1988 American Control Conference.

[6]  Thomas F. Edgar,et al.  Nonlinear model predictive control of a fixed-bed water-gas shift reactor: An experimental study , 1994 .

[7]  John W. Eaton,et al.  Model Predictive Control of Chemical Processes , 1991, 1991 American Control Conference.

[8]  Evanghelos Zafiriou,et al.  Observer based nonlinear quadratic dynamic matrix control for state space and input/output models , 1995 .

[9]  M. Morari,et al.  Robust model predictive control for nonlinear systems , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[10]  D. Mayne,et al.  Robust receding horizon control of constrained nonlinear systems , 1993, IEEE Trans. Autom. Control..

[11]  M. Morari,et al.  CONTROL OF NONLINEAR SYSTEMS SUBJECT TO INPUT CONSTRAINTS , 1995 .

[12]  B. Bequette.  Nonlinear control of chemical processes: a review , 1991 .

[13]  B. W. Bequette,et al.  Computational issues in nonlinear predictive control , 1993 .

[14]  A. Packard.  Gain scheduling via linear fractional transformations , 1994 .

[15]  J. Rawlings,et al.  Model-predictive control of chemical processes , 1992 .