ref-36bafee9274250697060surveyModel predictive control: Theory and practice—A survey
This abstract presents a survey of model predictive control as a family of controllers that directly use an explicit, separately identifiable model. Its organizing questions concern relationships among controller designs, constraint handling, performance objectives, nonlinear applicability and robustness. The authors emphasize easier adjustment for robustness while denying any inherent robustness advantage over classical feedback. These are abstract-level descriptions and author claims; the supplied material does not expose the arguments, examples or measurements needed to evaluate them (ev-definition, ev-survey-scope, ev-objectives, ev-nonlinear, ev-robustness, ev-source-scope).
This is a partial reading. Its claims are limited to the material listed in the coverage record.
The idea
The problem
The survey asks how MPC techniques address important control-system issues and what advantages they offer in design and implementation. The authors motivate this comparison by citing industrial adoption and the ability to sustain high-performance operation without continuous expert intervention. The abstract does not enumerate the complete set of issues or provide evidence measuring that operational benefit. ev-motivationev-survey-scopeev-source-scope
What this work contributes
The abstract defines MPC by direct use of an explicit, separately identifiable model. This establishes the survey boundary but supplies no controller implementation. ev-definitionev-source-scope
The announced survey first states important control-system issues and reviews MPC techniques against them. Its comparative contribution places Dynamic Matrix Control, Model Algorithmic Control, Inferential Control and Internal Model Control in relation to one another and examines their relationship to Linear Quadratic Control. The abstract names this comparison but does not state the resulting equivalences or differences. ev-survey-scope
The authors emphasize flexible constraint handling for process-industry operating objectives and announce discussion of 1-, 2-, and infinity-norm performance objectives. They also claim that the main attractions of MPC extend to nonlinear systems. These conclusions are announced without the supporting formulations or applicability conditions in the supplied abstract. ev-objectivesev-nonlinear
Source description summarizes the inspected material. Author claim preserves the authors’ attribution. Reader analysis and Open question are interpretive.
Taxonomy assessment
Catalog at reading time
Catalog updated
- Major category
- Foundational work
- Subcategories
- Theory & planning · Surveys & technical resources
- Architecture
- Not applicable
- Prediction paradigm
- Not applicable
- Quadrant
- Not applicable
- Classification status
- Explicit in survey
Classification assessment not applicable
The title and comparative remit support the catalog’s survey designation. Architecture, prediction-paradigm and quadrant assignments are not applicable to this survey-level description. The explicit-model definition explains its relevance to model-based control, but does not establish a learned world/action architecture, joint future/action prediction or inverse dynamics. The abstract alone also cannot establish the work’s historical influence implied by the foundational-work label. ev-identityev-definitionev-survey-scopeev-source-scope
These labels preserve the catalog snapshot used for this reading. The assessment audits that snapshot without changing the source classification.
Limits & reproduction
Limitations and open boundaries
The robustness statement is deliberately qualified: MPC is described as neither inherently more nor inherently less robust than classical feedback. Easier adjustment for robustness is a separate author claim. The abstract supplies neither a robustness definition nor a matched comparison that would establish the latter claim quantitatively. ev-robustnessev-source-scope
The nonlinear-extension claim does not identify a class of nonlinear systems, assumptions or failure conditions. Its scope cannot be assessed from the abstract. Likewise, the mention of alternative norms establishes topics covered, not measured superiority of one objective over another. ev-nonlinearev-objectivesev-source-scope
No numerical task, dataset, evaluation protocol, baseline result or uncertainty estimate appears in the supplied text. This report therefore records no empirical results; that absence describes the capture and does not establish that the full article lacks examples or experiments. ev-source-scope
What a reproduction would require
The abstract cannot specify a reproducible controller or benchmark. Evaluating its constraint-handling and robustness claims would require the actual model and controller definitions, performance objectives, constraints, tuning procedures, comparison conditions and outcome measures. None is given here; the named controller families alone are insufficient to select a faithful implementation. ev-survey-scopeev-objectivesev-robustnessev-source-scope
Questions to take further
- Which relationships among the four named controller families does the full survey establish, and under what assumptions? (ev-survey-scope)
- How does the full article distinguish easier robustness adjustment from robustness itself? (ev-robustness)
What was read
- Sections inspected
- Publisher capture: title and author byline
- Abstract (complete supplied text; one of one chunks)
- Appendix
- Not established
- Figures inspected
- No figures recorded as inspected
- Tables inspected
- No tables recorded as inspected
Outside this reading
- Only the publisher abstract was captured. Full methods, results, equations, tables, figures and references were not supplied.
- The source artifact is a browser-rendered text capture, not the original HTML or paper PDF. Its hash identifies that capture.
- No usable primary PDF was supplied. No PDF title page, figures, tables or crops were visually inspected; an illustrated edition cannot be produced from this capture.
- The exact title and all three authors match the inspected capture. The acquisition manifest identifies Automatica, Volume 25, Issue 3, May 1989, pages 335–348, DOI 10.1016/0005-1098(89)90002-2. These publication details are manifest provenance; the captured source itself contains no publication or revision header. No revised edition or revision history was supplied, so differences between article versions cannot be assessed.
- The full paper body and any appendices were not available for reading; whether the article has appendices is unknown. Affiliations were not supplied.
- Code was not inspected and experiments were not reproduced. The complete supplied abstract was read, which does not constitute a complete-paper review.
- The supplied abstract contains no state representation, model-identification procedure, objective equation, error-vector definition, optimization horizon, constraint set or feedback-update sequence. It does not support a detailed method, training or inference account, so all structured method fields remain empty.
Evidence & sources
Evidence links resolve to these source locations. Expand an entry to inspect its supporting detail.
ev-identityPublisher abstract capture, title and Authors line preceding [SECTION] Abstract
The title is Model predictive control: Theory and practice—A survey. The byline lists Carlos E. García; David M. Prett; Manfred Morari, matching the catalog title and authors.
Model predictive control: Theory and practice—A surveyev-definitionAbstract, sentence 1
The authors define MPC as a family of controllers that directly use an explicit, separately identifiable model.
Model predictive control: Theory and practice—A surveyev-motivationAbstract, sentences 2–3
The authors describe industrial acceptance and academic study of MPC, attributing its popularity to high-performance control over long periods without expert intervention.
Model predictive control: Theory and practice—A surveyev-survey-scopeAbstract, sentences 4–6
The survey states control-system issues, reviews MPC against them, compares Dynamic Matrix Control, Model Algorithmic Control, Inferential Control and Internal Model Control, and examines their relation to Linear Quadratic Control.
Model predictive control: Theory and practice—A surveyev-objectivesAbstract, sentence 7
The authors identify flexible constraint handling as an advantage for process-industry operating objectives and say they discuss 1-, 2-, and infinity-norm performance objectives.
Model predictive control: Theory and practice—A surveyev-nonlinearAbstract, sentence 8
The abstract says the application to nonlinear systems is examined and claims that MPC retains its principal attractions there; no conditions or examples are included in the capture.
Model predictive control: Theory and practice—A surveyev-robustnessAbstract, sentence 9
The authors say MPC is not inherently more or less robust than classical feedback, while claiming it can be adjusted more easily for robustness.
Model predictive control: Theory and practice—A surveyev-source-scope[BROWSER CAPTURE] marker and complete [SECTION] Abstract, sentences 1–9
The supplied artifact identifies itself as a publisher-rendered abstract capture rather than original HTML. It contains the title, author string and abstract only, with no PDF pages, original visuals, equations, quantitative results or implementation protocol.
Model predictive control: Theory and practice—A surveySource record
Model predictive control: Theory and practice—A survey
TEXT · 265 extracted words · Accessed 7 Sept 2026
Source URL & fingerprint
https://www.sciencedirect.com/science/article/abs/pii/0005109889900022
- SHA-256
529bd81f07daf28148c25e5dc6629f24b96466efa02847cc511857e9e35d981b