Details
| Original language | English |
|---|---|
| Pages (from-to) | 185-201 |
| Number of pages | 17 |
| Journal | Journal of Process Control |
| Volume | 92 |
| Early online date | 29 Jun 2020 |
| Publication status | Published - Aug 2020 |
Abstract
In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC scheme uses an online optimized artificial periodic orbit to ensure recursive feasibility and constraint satisfaction despite unpredictable changes in the economic performance index. We demonstrate that the direct extension of existing methods to periodic orbits does not necessarily yield the desirable closed-loop economic performance. Instead, we carefully revise the constraints on the artificial trajectory, which ensures that the closed-loop average performance is no worse than a locally optimal periodic orbit. In the special case that the prediction horizon is set to zero, the proposed scheme is a modified version of recent publications using periodicity constraints, with the important difference that the resulting closed loop has more degrees of freedom which are vital to ensure convergence to an optimal periodic orbit. In addition, we detail a tailored offline computation of suitable terminal ingredients, which are both theoretically and practically beneficial for closed-loop performance improvement. Finally, we demonstrate the practicality and performance improvements of the proposed approach on benchmark examples.
Keywords
- Changing economic criteria, Dynamic real time optimization, Economic model predictive control, Nonlinear model predictive control, Periodic optimal control
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Modelling and Simulation
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In: Journal of Process Control, Vol. 92, 08.2020, p. 185-201.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Periodic optimal control of nonlinear constrained systems using economic model predictive control
AU - Köhler, Johannes
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: This work was supported by the German Research Foundation (DFG) under Grants GRK 2198/1 - 277536708 , AL 316/12-2 , and MU 3929/1-2 - 279734922 .
PY - 2020/8
Y1 - 2020/8
N2 - In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC scheme uses an online optimized artificial periodic orbit to ensure recursive feasibility and constraint satisfaction despite unpredictable changes in the economic performance index. We demonstrate that the direct extension of existing methods to periodic orbits does not necessarily yield the desirable closed-loop economic performance. Instead, we carefully revise the constraints on the artificial trajectory, which ensures that the closed-loop average performance is no worse than a locally optimal periodic orbit. In the special case that the prediction horizon is set to zero, the proposed scheme is a modified version of recent publications using periodicity constraints, with the important difference that the resulting closed loop has more degrees of freedom which are vital to ensure convergence to an optimal periodic orbit. In addition, we detail a tailored offline computation of suitable terminal ingredients, which are both theoretically and practically beneficial for closed-loop performance improvement. Finally, we demonstrate the practicality and performance improvements of the proposed approach on benchmark examples.
AB - In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC scheme uses an online optimized artificial periodic orbit to ensure recursive feasibility and constraint satisfaction despite unpredictable changes in the economic performance index. We demonstrate that the direct extension of existing methods to periodic orbits does not necessarily yield the desirable closed-loop economic performance. Instead, we carefully revise the constraints on the artificial trajectory, which ensures that the closed-loop average performance is no worse than a locally optimal periodic orbit. In the special case that the prediction horizon is set to zero, the proposed scheme is a modified version of recent publications using periodicity constraints, with the important difference that the resulting closed loop has more degrees of freedom which are vital to ensure convergence to an optimal periodic orbit. In addition, we detail a tailored offline computation of suitable terminal ingredients, which are both theoretically and practically beneficial for closed-loop performance improvement. Finally, we demonstrate the practicality and performance improvements of the proposed approach on benchmark examples.
KW - Changing economic criteria
KW - Dynamic real time optimization
KW - Economic model predictive control
KW - Nonlinear model predictive control
KW - Periodic optimal control
UR - http://www.scopus.com/inward/record.url?scp=85087036502&partnerID=8YFLogxK
U2 - 10.1016/j.jprocont.2020.06.004
DO - 10.1016/j.jprocont.2020.06.004
M3 - Article
VL - 92
SP - 185
EP - 201
JO - Journal of Process Control
JF - Journal of Process Control
SN - 0959-1524
ER -