Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm

Zhang, Yunfan; Zhou, Quan; Li, Ji; Xu, Hongming

doi:10.1177/0954407018776745

Cited by 9 publications

(10 citation statements)

References 39 publications

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“…A larger α ( 0 ) is able to achieve a larger α ( i ) in order to escape from the locally optimum results effectively. Based on authors’ repeatability studies and previous works, 24,25 α ( 0 ) = 0 . 9 and γ = 0 . 8 are selected in this article.…”

Section: The Optimization Algorithmmentioning

confidence: 99%

“…Based on authors’ previous researches, 24,25 α and initial β ( 1 ) are set as α = 4 and β ( 1 ) = 0 . 7 , respectively, to highly improve the randomization for the generated particles.…”

Section: The Optimization Algorithmmentioning

confidence: 99%

“…29,30 Many researchers have validated that the CAPSO algorithm performs much better than the conventional PSO algorithm. 24,25,31…”

Section: Introductionmentioning

confidence: 99%

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Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Zhou

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.

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Section: The Optimization Algorithmmentioning

confidence: 99%

Section: The Optimization Algorithmmentioning

confidence: 99%

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Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Zhou

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…It can take an advantage of maximising ∆RFM compensation on AFR control system, especially for stabilising fluctuation caused by transient changes in working scenarios. At last, the CA-based MF pattern with = 1, = 2 is drawn in Fig 9. The sensitivity of CA-based MF is compared with two typical MF patterns [28]…”

Section: Membership Function Suitability Assessmentmentioning

confidence: 99%

“…Our previous work [25–28] shows although GDI engines performance can be improved through intelligent calibration, their transient behaviours cannot be easily described quantitatively and the adaptability of its control system needs to be improved. To develop robustness of the engine control system, so far an FLC has been introduced with direct methods drawing on expert knowledge to provide fuzzy set membership scores [9, 10].…”

Section: Introductionmentioning

confidence: 99%

Improved scheme of membership function optimisation for fuzzy air‐fuel ratio control of GDI engines

Zhou

Zhang

et al. 2018

IET Intelligent Transport Systems

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This paper researches an improved scheme of Membership Function Optimisation (MFO) for fuzzy Air-fuel Ratio (AFR) control of Gasoline Direct Injection (GDI) engines based on Correspondence Analysis (CA). This PI-like Fuzzy Knowledge-Based Controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the Three-Way Catalyst (TWC) to eliminate the exhaust emissions in real-time. Different from the conventional experience-based Membership Function Design (MFD) method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: 1) Specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution. 2) Based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively. 3) The engine controller with the proposed scheme is real-time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.

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Automated function development for emission control with deep reinforcement learning

Koch¹,

Picerno²,

Badalian³

et al. 2023

Engineering Applications of Artificial Intelligence

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Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm

Cited by 9 publications

References 39 publications

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Improved scheme of membership function optimisation for fuzzy air‐fuel ratio control of GDI engines

Automated function development for emission control with deep reinforcement learning

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