Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations

Lars Grüne; Luca Mechelli; Simon Pirkelmann; Stefan Volkwein

doi:10.3934/mcrf.2021013

Article Contents

2021, Volume 11, Issue 3: 579-599. Doi: 10.3934/mcrf.2021013

This issue Previous Article Optimal control of ODEs with state suprema Next Article A priori error estimates for the space-time finite element discretization of an optimal control problem governed by a coupled linear PDE-ODE system

Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations

*.
University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany
**.
University of Konstanz, Universitätsstraße 10, 78464 Konstanz, Germany

Received: May 2019

Revised: January 2020

Early access: March 2021

Published: September 2021

The authors are supported by DFG grants GR 1569/16-1 and VO 1658/4-1

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Abstract

In this paper performance indices for economic model predictive controllers (MPC) are considered. Since existing relative performance measures, designed for stabilizing controllers, fail in the economic setting, we propose alternative absolute quantities. We show that these can be applied to assess the performance of the closed loop trajectories on-line while the controller is running. The advantages of our approach are demonstrated by simulations involving a convection-diffusion-system. The method is also combined with proper orthogonal decomposition, thus demonstrating the possibility for both efficient and performant MPC for systems governed by partial differential equations.

Keywords:

Economic model predictive control,
proper orthogonal decomposition.

Mathematics Subject Classification: Primary: 93-08, 93B52.

Citation:

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Figure 1. Illustration of the quantities used for the computation of the performance indices $ \varepsilon_{N,P}^2(K) $ and $ \varepsilon_{N,P}^3(K) $

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Figure 2. Explanation of how the choice of $ P $ influences the quantity $ E_{N,P} $

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Figure 3. Boundary $ \Gamma = \Gamma_{\mathsf{out}}\cup \Gamma_{\mathsf{c}} $ and velocity field $ \boldsymbol v(x) $ (green = inflow, blue = outflow)

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Figure 4. MPC closed loop cost and relative performance index

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Figure 5. Plots of the individual components of the absolute performance index

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Figure 6. Plots of performance index $ \varepsilon^3_{N,P} $ for longer simulation times and of the influence of $ P $ on the value of $ E_{N,P} $

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Figure 7. Comparison between POD-FE and POD-POD relative errors for $ \sum_{k = 0}^{K-1} \varepsilon^1_N(k) $ with $ \ell = 25 $ POD basis functions

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Figure 8. Relative error between the FE and POD cumulative performance index $ \varepsilon_N^1 $ over time $ k $ for different $ N $ and number of POD basis functions $ \ell = 15,20,25 $

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Figure 9. Relative error between the FE and POD $ E_{N,P} $ over time $ k $ for $ P = 30 $, different $ N $ and number of POD basis functions $ \ell = 15,20,25 $

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Table 1. Test results for $ N = 200 $, $ P = 30 $ and $ K = 120 $ for full (FE) and reduced (POD) order models

Method	$ \ell $	rel_err_$ u $	$ J_{120}^{cl}(y_{\circ},\mu_{200}) $	$ E_{200,30}(30) $	Alg. Time	Speed-up
FE	–	–	7791.565	-0.0014	1182 s	–
POD-FE	20	0.00055	7791.591	0.0031	346 s	3.4
POD-FE	25	0.00008	7791.569	-0.0041	396 s	3.0
POD-POD	20	0.00070	7791.614	0.0042	312 s	3.8
POD-POD	25	0.00012	7791.577	-0.0038	302 s	3.9

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