A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines

Janakiraman, Vijay Manikandan; Nguyen, Xuan Long; Sterniak, Jeff; Assanis, Dennis N.

doi:10.1007/978-3-319-03500-0_19

Cited by 7 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, OS-ELM doesn't have any control over the parameter evolution. It is governed by the evolution of the co-variance matrix M (14). It is expected that the co-variance matrix M would add stability to the parameter evolution but in practice, it tends to be more aggressive leading to potential instabilities as reported by [22], [23], [24], [25].…”

Section: B Results and Discussionmentioning

confidence: 99%

“…The parameters of each of the models are tuned to accurately represent the given dataset. As recommended by OS-ELM [1], about 800 cycles of data was used for initializing the output layer parameters W 0 and covariance matrix M 0 (see equations (14) and (15)). The initialization was performed using the batch ELM algorithm [30].…”

Section: A Model Structure and Evaluation Metricmentioning

confidence: 99%

“…To address the issue, advanced model based control methods are common where the control actions are often made using a predictive model of the engine [11], [6], [12]. As alternatives to physics based modeling that might involve significant development time and associated costs, data based approaches were introduced [13], [14], [15] that takes advantage of the extensive experimentation that is performed during the engine calibration process.…”

Section: Introductionmentioning

confidence: 99%

“…The envelope model can be used to alarm the onboard diagnostics if the engine is about to misfire owing to changes in system or operating conditions. Data based modeling approaches for the HCCI engine state variables and dynamic operating envelope were demonstrated using neural networks [13], support vector machines [14], extreme learning machines [21] by the authors. However, the previous research considered an offline approach where the data collected from engine experiments were taken offline and models were developed using computer workstations that had high processing and memory.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines

2016

Self Cite

View full text Add to dashboard Cite

In this article, a stochastic gradient based online learning algorithm for Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion based on Lyapunov approach is used to prove both asymptotic stability of estimation error and stability in the estimated parameters suitable for identification of nonlinear dynamic systems. The developed algorithm not only guarantees stability, but also reduces the computational demand compared to the OS-ELM approach [1] based on recursive least squares. In order to demonstrate the effectiveness of the algorithm on a real-world scenario, an advanced combustion engine identification problem is considered. The algorithm is applied to two case studies: An online regression learning for system identification of a Homogeneous Charge Compression Ignition (HCCI) Engine and an online classification learning (with class imbalance) for identifying the dynamic operating envelope of the HCCI Engine. The results indicate that the accuracy of the proposed SG-ELM is comparable to that of the state-of-the-art but adds stability and a reduction in computational effort.

show abstract

Section: B Results and Discussionmentioning

confidence: 99%

Section: A Model Structure and Evaluation Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The data is sampled using the AVL Indiset acquisition system where in-cylinder pressure is sensed every crank angle while NMEP, CA50 and R max are determined on a per-combustion cycle basis. More details on HCCI combustion and experiments can be found in [16]. The data is pre-processed and labeled to identify stable and unstable observations as explained in section III-C.…”

Section: A Hcci System and Experimentationmentioning

confidence: 99%

Identification of the Dynamic Operating Envelope of HCCI Engines Using Class Imbalance Learning

Janakiraman

Nguyen

Sterniak

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

View full text Add to dashboard Cite

Homogeneous charge compression ignition (HCCI) is a futuristic automotive engine technology that can significantly improve fuel economy and reduce emissions. HCCI engine operation is constrained by combustion instabilities, such as knock, ringing, misfires, high-variability combustion, and so on, and it becomes important to identify the operating envelope defined by these constraints for use in engine diagnostics and controller design. HCCI combustion is dominated by complex nonlinear dynamics, and a first-principle-based dynamic modeling of the operating envelope becomes intractable. In this paper, a machine learning approach is presented to identify the stable operating envelope of HCCI combustion, by learning directly from the experimental data. Stability is defined using thresholds on combustion features obtained from engine in-cylinder pressure measurements. This paper considers instabilities arising from engine misfire and high-variability combustion. A gasoline HCCI engine is used for generating stable and unstable data observations. Owing to an imbalance in class proportions in the data set, the models are developed both based on resampling the data set (by undersampling and oversampling) and based on a cost-sensitive learning method (by overweighting the minority class relative to the majority class observations). Support vector machines (SVMs) and recently developed extreme learning machines (ELM) are utilized for developing dynamic classifiers. The results compared against linear classification methods show that cost-sensitive nonlinear ELM and SVM classification algorithms are well suited for the problem. However, the SVM envelope model requires about 80% more parameters for an accuracy improvement of 3% compared with the ELM envelope model indicating that ELM models may be computationally suitable for the engine application. The proposed modeling approach shows that HCCI engine misfires and high-variability combustion can be predicted ahead of time, given the present values of available sensor measurements, making the models suitable for engine diagnostics and control applications.

show abstract

Design, development and testing a hybrid control model for RCCI engine using double Wiebe function and random forest machine learning

Mishra

Subbarao

2021

Control Engineering Practice

View full text Add to dashboard Cite

A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines

Cited by 7 publications

References 8 publications

Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines

Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines

Identification of the Dynamic Operating Envelope of HCCI Engines Using Class Imbalance Learning

Design, development and testing a hybrid control model for RCCI engine using double Wiebe function and random forest machine learning

Contact Info

Product

Resources

About