Optimization approaches to nonlinear model predictive control

TLDR: This paper explores and reviews control techniques based on repeated solution of nonlinear programming (NLP) problems and presents a basic algorithm for optimization-based process control, a straightforward extension of popular model-predictive controllers that are used for linear systems.

Abstract: With the development of sophisticated methods for nonlinear programming and powerful computer hardware, it now becomes useful and efficient to formulate and solve nonlinear process control problems through on-line optimization methods This paper explores and reviews control techniques based on repeated solution of nonlinear programming (NLP) problems Here several advantages present themselves These include minimization of readily quantifiable objectives, coordinated and accurate handling of process nonlinearities and interactions, and systematic ways of dealing with process constraints We motivate this NLP-based approach with small nonlinear examples and present a basic algorithm for optimization-based process control As can be seen this approach is a straightforward extension of popular model-predictive controllers (MPCs) that are used for linear systems The statement of the basic algorithm raises a number of questions regarding stability and robustness of the method, efficiency of the control calculations, incorporation of feedback into the controller and reliable ways of handling process constraints Each of these will be treated through analysis and/or modification of the basic algorithm To highlight and support this discussion, several examples are presented and key results are examined and further developed 74 refs, 11 figs