Model predictive control: past, present and future

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More than 15 years after model predictive control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started to emerge. The issues of feasibility of the on-line optimization, stability and performance are largely understood for systems described by linear models. Much progress has-been made on these issues for non-linear systems but for practical applications many questions remain, including the reliability and efficiency of the on-line computation scheme. To deal with model uncertainty 'rigorously' an involved dynamic programming problem must be solved. The approximation techniques proposed for this purpose are largely at a conceptual stage. Among the broader research needs the following areas are identified: multivariable system identification, performance monitoring and diagnostics, non-linear state estimation, and batch system control. Many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated systematically and effectively into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program. Efficient techniques for solving these problems are becoming available. (C) 1999 Elsevier Science Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1999-05
Language
English
Article Type
Article
Keywords

CONSTRAINED NONLINEAR-SYSTEMS; RECEDING HORIZON CONTROL; 2ND-ORDER VOLTERRA MODELS; LINEAR-SYSTEMS; PLANT UNCERTAINTY; INPUT SATURATION; CONTROL STRATEGIES; HARD CONSTRAINTS; STATE ESTIMATION; IDENTIFICATION

Citation

COMPUTERS CHEMICAL ENGINEERING, v.23, no.4-5, pp.667 - 682

ISSN
0098-1354
DOI
10.1016/S0098-1354(98)00301-9
URI
http://hdl.handle.net/10203/73936
Appears in Collection
CBE-Journal Papers(저널논문)
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