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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

A Robust Design Optimization Framework for Systematic Model-Based Calibration of Engine Control Systems

[+] Author and Article Information
Hoseinali Borhan

Control Systems Research,
Research and Technology (R&T),
Cummins Inc.,
Columbus, IN 47201
e-mail: hoseinali.borhan@cummins.com

Edmund Hodzen

Advanced Control Systems Engineering,
Research and Technology (R&T),
Cummins Inc.,
Columbus, IN 47201
e-mail: edmund.p.hodzen@cummins.com

Contributed by the Controls, Diagnostics and Instrumentation Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 27, 2015; final manuscript received March 30, 2015; published online May 12, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(11), 111601 (Nov 01, 2015) (7 pages) Paper No: GTP-15-1058; doi: 10.1115/1.4030396 History: Received February 27, 2015; Revised March 30, 2015; Online May 12, 2015

In this paper, a systematic model-based calibration framework basing on robust design optimization technique is developed for engine control system. In this framework, the control system is calibrated in an optimization fashion where both performance and robustness of the closed-loop system to uncertainties are optimized. The proposed calibration process has three steps: in the first step, the optimal performance of the system at the nominal conditions, where the effects of uncertainties are ignored, is computed by formulation of the controller calibration as an optimization problem. The capabilities of the controller are fully explored at nominal conditions. In the second step, the robustness and sensitivity of a selected control design to the system uncertainties are analyzed using Monte Carlo simulation. In the third step, robust design optimization is applied to optimize both performance and robustness of the closed-loop system to the uncertainties. The robustness capabilities of the controller are fully explored and the one that satisfies both performance and robustness requirements is selected. This process is implemented for the calibration of an advanced diesel air path control system with a variable geometry turbocharger (VGT) and dual loop exhaust gas recirculation (EGR) architecture.

Copyright © 2015 by ASME
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References

Karl, J. A., and Kumar, P. R., 2014, “Control: A Perspective,” Automatica, 50(1), pp. 3–43. [CrossRef]
Beyer, H., and Sendhoff, B., 2007, “Robust Optimization—A Comprehensive Survey,” Comput. Methods Appl. Mech. Eng., 196(33–34), pp. 3190–3218. [CrossRef]
Metropolis, N. C., and Ulam, S. M., 1949, “The Monte Carlo Method,” J. Am. Stat. Assoc., 44(247), pp. 335–341. [CrossRef] [PubMed]
Ortner, P., 2005, “MPC for a Diesel Engine Airpath Using an Explicit Approach for Constraint Systems,” Diploma thesis, Institute for Design and Control Mechatronical Systems, Johannes Kepler University, Linz, Austria.
Ortner, P., and Re, L., 2007, “Predictive Control of a Diesel Engine Air Path,” IEEE Trans. Control Syst. Technol., 15(3), pp. 449–456. [CrossRef]
Stewart, G., and Borrelli, F., 2008, “A Model Predictive Control Framework for Industrial Turbodiesel Engine Control,” 47th IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexico, Dec. 9–11, pp. 5704–5711. [CrossRef]
Borhan, H., Kothandaraman, G., and Pattel, B., 2015, “Air Handling Control of a Diesel Engine With a Complex Dual-Loop EGR and VGT Air System Using MPC,” IEEE American Control Conference, Chicago, IL, July 1–3.
Suresh, A., Langenderfer, D., Arnett, C., and Ruth, M., 2013, “Thermodynamic Systems for Tier 2 Bin 2 Diesel Engines,” SAE Int. J. Engines, 6(1), pp. 167–183. [CrossRef]
Esteco,modefrontier Version 4.4.1 Documentation,” Esteco, Novi, MI, http://www.esteco.com

Figures

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Fig. 1

A schematic view of the engine air path system [8]

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Fig. 2

Air path control system architecture

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Fig. 3

An illustration of operating region division for the switched MPC design

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Fig. 4

Control system input/output structure

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Fig. 5

Schematic view of step 1—control system calibration optimization at the nominal conditions

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Fig. 6

Schematic view of step 2—impact of uncertainties

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Fig. 7

Schematic view of step 3—robust calibration optimization

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Fig. 8

Calibration optimization results of the closed-loop control system

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Fig. 9

Deviation of control system performance due to the uncertainties

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Fig. 10

Probability distribution of the charge flow tracking error due to the uncertainties

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Fig. 11

Probability distribution of the EGR flow tracking error due to the uncertainties

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Fig. 12

Schematic of the robustness objectives of the control system

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Fig. 13

Sensitivity analysis results with respect to charge flow tracking

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Fig. 14

Sensitivity analysis results with respect to EGR flow tracking

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Fig. 15

Pareto robust optimal solutions of the robust calibration optimization of the control system

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Fig. 16

Robustness performance of a selected calibration design from the Pareto robust optimal solutions

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Fig. 17

Closed-loop control performance results for a selected calibration design

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Fig. 18

Actuator commands over the engine driving cycle (FF is the feedforward and FB is the feedback control inputs)

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