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Constrained model predictive control: Stability and optimality (2000)
| Venue: | AUTOMATICA |
| Citations: | 738 - 16 self |
BibTeX
@ARTICLE{Mayne00constrainedmodel,
author = {D. Q. Mayne and J. B. Rawlings and C. V. Rao and P. O. M. Scokaert},
title = { Constrained model predictive control: Stability and optimality},
journal = {AUTOMATICA},
year = {2000},
volume = {36},
pages = {789--814}
}
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Abstract
Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant. An important advantage of this type of control is its ability to cope with hard constraints on controls and states. It has, therefore, been widely applied in petro-chemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and/or time-varying systems. We concentrate our attention on research dealing with stability and optimality; in these areas the subject has developed, in our opinion, to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control. We distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved on-line is exactly equivalent to the same problem with an infinite horizon; in other cases it is equivalent to a modified infinite horizon optimal control problem. In both situations, known advantages of infinite horizon optimal control accrue.
Keyphrases
survey paper predictive control nite horizon optimal control problem model predictive control nite horizon optimal control accrue su cient maturity brie model predictive control nite horizon concise characterization model predictive controller ciency demand admissible state modi ed initial state current control action hard constraint nonlinear control important advantage related industry extensive literature essential principle constrained system active interest current state optimal control sequence elsevier science ltd time-varying system
