On-line Feedforward Map Generation for Engine Ignition Timing Control

Tamaki, Shunpei; Sakayanagi, Yoshihiro; Sekiguchi, Kazuma; Ibuki, Tatsuya; Tahara, Kohei; Sampei, Mitsuji

doi:10.3182/20140824-6-za-1003.01886

Cited by 23 publications

(6 citation statements)

References 13 publications

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“…The fuel injection map can be used as the initial manifold absolute pressure (MAP) diagram, which is commonly used in the development of electronic control systems for gasoline engines. 35,36 The Model-PA performance simulation results indicate that the adjusted general model of the two-stroke engine's power output accurately reflects the HIRTH-3203's static and dynamic characteristics. Model-PA is well suited for studying the power output and speed control algorithms of two-stroke aviation engines because the general model is composed of empirical formulas, significantly reducing the modeling workload.…”

Section: Model-pa Performancementioning

confidence: 95%

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Modeling of two-stroke aviation piston engines for control applications

Jiang

Zhou

et al. 2023

Advances in Mechanical Engineering

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Two-stroke Aviation Piston Engines are multivariable systems with severe non-linear dynamics, making their modeling challenging for control engineers. Although many studies have been conducted on simplified modeling of four-stroke gasoline engines and large-scale two-stroke diesel engines for automobiles and ships, only a few have focused on the general modeling of small two-stroke aviation engines. Thus, extensive research on a general modeling method for two-stroke aviation engines is required. A general non-linear Mean Value Engine Model (MVEM) of two-stroke aviation piston engines is developed. Various parts of the model are represented by appropriate empirical equations that require little engine data and easily fit different engines. The objective is to develop a general and accurate method for rapid engine modeling that captures the main dynamics and can create control systems for two-stroke aviation piston engines. The model is validated using HIRTH-3203 and NU-57 measurement data, and the results show that the issues of fitting simplicity and general applicability are well addressed. Finally, the air-fuel ratio control based on the model predictive control method is carried out on the HIRTH-3203 MVEM, which demonstrates the effectiveness of the proposed MVEM in the controller design and evaluation. The proposed model may support the application of new control technologies, such as adaptive control and intelligent control, in the two-stroke aviation piston engine systems.

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Section: Model-pa Performancementioning

confidence: 95%

Section: General Model Adjustment and Validationmentioning

confidence: 99%

Modeling of two-stroke aviation piston engines for control applications

Jiang

Zhou

et al. 2023

Advances in Mechanical Engineering

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“…Similarly to DI, the single PFI look-up table is updated for coping with eventual bias over time. The strategy is based on a principle which was used in timing control applications for spark ignition engines (Tamaki et al, 2014;Gao et al, 2017). Particularly, it was decided to learn from the value given by the model and update the look-up table utilizing a Gaussian filter in order to adapt the complete grid of the table.…”

Section: Adaptive Look-up Tablesmentioning

confidence: 99%

Individual cylinder fuel blend estimation in a dual-fuel engine using an in-cylinder pressure based observer

Guardiola

Plá

Bares

et al. 2021

Control Engineering Practice

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“…Alternatives to control with fuel maps are learningbased control that enables particular fuels to be combusted under multiple operating conditions [10][11][12][13][14][15][16][17][18][19][20][21] or fluctuating operating conditions. [22][23][24][25][26][27][28][29] The learning-based control includes acquiring training data in advance by manually operating an actual engine or simulation under all possible operating conditions and generating a combustion model that determines the ignition timing, fuel flow rate, and air flow rate (the ''control parameters'') corresponding to the target rotation speed and torque. This means that in order to directly apply the learning-based control to the reforming engine without any modification, training data must be acquired for all potential types of fuels and potential mixing ratios combusted in the reforming engine in advance.…”

Section: Introductionmentioning

confidence: 99%

Active-exploration-based generation of combustion-model adjusting to occasional rapid changes in mixed ratio of various fuel types

Esaki

Ito

Shirakawa

et al. 2020

International Journal of Engine Research

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An active-exploration-based method has been developed for an engine combusting fuels with occasional rapid changes in a mixed ratio of various fuel types. To apply learning-based control to the engine, training data should be acquired and combustion models should be generated during actual operation. Occasional rapid changes in fuels lead to a small amount of data for each fuel type and ratio, which is susceptible to noise and results in slow convergence in exploring training data. Furthermore, slow convergence leads to extended control using no-longer appropriate parameters, causing faults. The method reduces the negative effect of noise by discretizing the combustion state space and naturally avoids engine faults by predicting the no-fault condition (i.e. stable combustion with higher thermal efficiency). Numerical experiments of typical optimization problems showed that the proposed method is robust against noise during the optimization process. By applying the method to an actual engine combusting a changing mixture of hydrogen and ethanol, thermal efficiency after combustion-model generation increased up to 7.9% and the coefficient of variation of the indicated mean effective pressure was less than 3% for all ratios. The increase in thermal efficiency corresponds to continuous predictions of the no-faults condition. These results demonstrate the potential practical use of engines with occasional rapid changes in mixed ratios of various fuel types during operation.

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On-line Feedforward Map Generation for Engine Ignition Timing Control

Cited by 23 publications

References 13 publications

Modeling of two-stroke aviation piston engines for control applications

Modeling of two-stroke aviation piston engines for control applications

Individual cylinder fuel blend estimation in a dual-fuel engine using an in-cylinder pressure based observer

Active-exploration-based generation of combustion-model adjusting to occasional rapid changes in mixed ratio of various fuel types

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