Details
| Original language | English |
|---|---|
| Title of host publication | 2016 European Control Conference, ECC 2016 |
| Pages | 1322-1327 |
| Number of pages | 6 |
| ISBN (electronic) | 9781509025916 |
| Publication status | Published - 1 Jun 2016 |
Abstract
Keywords
- control system synthesis, predictive control, statistical analysis, stochastic systems, unrestricted horizon predictive control, fully stochastic model-based predictive approach, UHPC design, minimal order generalized minimum variance controller, Diophantine equation, ARMAX model, state space solution, 1-step ahead Kalman filter, Mathematical model, Stochastic processes, Predictive models, Kalman filters, Predictive control, Aerospace electronics
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Control and Systems Engineering
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2016 European Control Conference, ECC 2016. 2016. p. 1322-1327 7810472.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On the unrestricted horizon predictive control — A fully stochastic model-based predictive approach
AU - Trentini, R.
AU - Silveira, A.
AU - Kutzner, R.
AU - Hofmann, L.
N1 - Publisher Copyright: © 2016 EUCA. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - This paper presents the design of the Unrestricted Horizon Predictive Controller, short UHPC, evaluated from the extended horizon of a minimal order Generalized Minimum Variance controller investigated in a free Diophantine equation form based on ARMAX models. In contrast to common MPC methods, UHPC does not employ the receding horizon approach for calculation of its future control steps, but a state space solution where a 1-step ahead Kalman Filter and the inherent calculation of two Diophantine equations - one for the control signal and other for the noise - are utilized. Simulation results show that UHPC performs well compared to the most common approaches (Minimum Variance and Generalized Predictive Control) for several different plant conditions, such as regulation (noise rejection) and reference tracking.
AB - This paper presents the design of the Unrestricted Horizon Predictive Controller, short UHPC, evaluated from the extended horizon of a minimal order Generalized Minimum Variance controller investigated in a free Diophantine equation form based on ARMAX models. In contrast to common MPC methods, UHPC does not employ the receding horizon approach for calculation of its future control steps, but a state space solution where a 1-step ahead Kalman Filter and the inherent calculation of two Diophantine equations - one for the control signal and other for the noise - are utilized. Simulation results show that UHPC performs well compared to the most common approaches (Minimum Variance and Generalized Predictive Control) for several different plant conditions, such as regulation (noise rejection) and reference tracking.
KW - control system synthesis
KW - predictive control
KW - statistical analysis
KW - stochastic systems
KW - unrestricted horizon predictive control
KW - fully stochastic model-based predictive approach
KW - UHPC design
KW - minimal order generalized minimum variance controller
KW - Diophantine equation
KW - ARMAX model
KW - state space solution
KW - 1-step ahead Kalman filter
KW - Mathematical model
KW - Stochastic processes
KW - Predictive models
KW - Kalman filters
KW - Predictive control
KW - Aerospace electronics
UR - http://www.scopus.com/inward/record.url?scp=85015088336&partnerID=8YFLogxK
U2 - 10.1109/ecc.2016.7810472
DO - 10.1109/ecc.2016.7810472
M3 - Conference contribution
SP - 1322
EP - 1327
BT - 2016 European Control Conference, ECC 2016
ER -