Abstract
Constrained receding-horizon predictive control (CRHPC) is intended for demanding control applications where conventional predictive control designs can fail. The idea behind CRHPC is to optimise a quadratic function over a 'costing horizon' subject to the condition that the output matches the reference value over a further constraint range. Theorems show that the method stabilises general linear plants (e.g. unstable, nonminimum-phase, dead-time). Simulation studies demonstrate good behaviour with even nearly unobservable systems (where generalised predictive control is ineffective) and that control-costing is a particularly effective tuning parameter.
Keywords
Model predictive controlUnobservableControl theory (sociology)HorizonConstraint (computer-aided design)Activity-based costingControl (management)Predictive valueMathematical optimizationComputer scienceMathematicsEconomicsEconometricsArtificial intelligence
Affiliated Institutions
Related Publications
Random Allocation in Observational Data
Conventional observational epidemiology has an unenviable reputation for generating false-positive findings,1,2 or "scares," as others call them.3 In 1993, for example, the New ...
Publication Info
- Year
- 1991
- Type
- article
- Volume
- 138
- Issue
- 4
- Pages
- 347-347
- Citations
- 321
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
321
OpenAlex
Cite This
D.W. Clarke,
Riccardo Scattolini
(1991).
Constrained receding-horizon predictive control.
IEE Proceedings D Control Theory and Applications
, 138
(4)
, 347-347.
https://doi.org/10.1049/ip-d.1991.0047
Identifiers
- DOI
- 10.1049/ip-d.1991.0047