A Nonlinear Model Predictive Control Strategy with a Disturbance Observer for Spark Ignition Engines with External EGR

Zhu, Qilun; Prucka, Robert; Prucka, Michael; Dourra, Hussein

doi:10.4271/2017-01-0608

Cited by 8 publications

(11 citation statements)

References 39 publications

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“…MPC has been used in ICE control for a wide variety of ICE control problems. Among these control problems, MPC has been used widely for the control of fuel consumption [28,30,[33][34][35][36]52,54,[135][136][137], combustion phasing [21,22,27,28,[37][38][39]52,54,108,129,130,138], cyclic variability [54,130], torque and load (IMEP) [22,27,28,33,[37][38][39][40][41][42]52,54,108,129,130,[136][137][138], idle speed [25,36,43,139,140], airpath (P man , EGR)…”

Section: Mpc Applications In Icesmentioning

confidence: 99%

“…MPC applications are not limited to a specific engine type, and, due to the capability of MPC, they have been implemented in various types of engines. MPC has been implemented for different ICE combustion modes, including Compression Ignition Combustion (CI) [26,[28][29][30][31]34,35,40,45,[47][48][49][50]53,135,137,139,[141][142][143]145,147,148,150], Spark Ignition (SI) [25,33,36,[41][42][43]51,52,54,136,138,140,144], and Low Temperature Combustion (LTC) [21,22,27,[37][38][39]108,129,130].…”

Section: Mpc Applications In Icesmentioning

confidence: 99%

“…MPC has also been implemented in wide varieties of engine size ranging including a single cylinder [37,108,138], four cylinder [21,22,25,27,35,42,45,[51][52][53]129,130,137,139,140,143], six cylinder [28][29][30]54,135,147], and eight cylinder [36,43,149] [21,22,27,28,37,53,108,129,130,137,138,143], 2.0 < V d ≤ 7.0 [25,30,31,35,36,38,39,41,42,45,51,<...>…”

Section: Mpc Applications In Icesmentioning

confidence: 99%

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Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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Section: Mpc Applications In Icesmentioning

confidence: 99%

Section: Mpc Applications In Icesmentioning

confidence: 99%

Section: Mpc Applications In Icesmentioning

confidence: 99%

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Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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“…However, the cooled external EGR causes an unwanted coupling and nonlinear effect on the fresh air charging and combustion (6)(7)(8) , and results in a significant challenge on the control of the combustion. To make things worse, it may trigger unstable combustion without carefully controlling (1,9) . To try to handle this challenge issue, a lot of studies have focused on the sense of mean value model via dynamic system control (6)(7)(8) .…”

Section: Introductionmentioning

confidence: 99%

“…On the sense of the cyclic transient behavior, disturbance rejection-based feedback control (10) , MPC based (9) , extremum seeking (11 ) , adaptive control (12) etc. have been studied.…”

Section: Introductionmentioning

confidence: 99%

Air Path Dynamics Control and Efficiency Optimization with Scenario Approach for Gasoline Engines

Jiang

Shen

2019

IJAE

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This paper presents a control design approach of efficiency optimization while providing the desired torque under the constraint of combustion variation for gasoline engines with EGR. Mainly, the first phase of control design is the equilibrium point optimization under the chance constraint of IMEP probability distribution with a scenario approach, and the second phase is nonlinear dynamic feedback stabilization control design for improving transient performance of the state regulation. The experimental validations demonstrate that the control scheme can achieve stable combustion and fast transient control performance.

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Deep learning based model predictive control for compression ignition engines

Norouzi

Shahpouri²,

Gordon³

et al. 2022

Control Engineering Practice

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A Nonlinear Model Predictive Control Strategy with a Disturbance Observer for Spark Ignition Engines with External EGR

Cited by 8 publications

References 39 publications

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Air Path Dynamics Control and Efficiency Optimization with Scenario Approach for Gasoline Engines

Deep learning based model predictive control for compression ignition engines

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