More Like this

1. Data-Driven Modeling and Predictive Control of Combustion Phasing for RCCI Engines

   Khoshbakht Irdmousa, Behrouz ; Rizvi, Syed Z ; Mohammadpour Velni, Javad ; Naber, Jeffrey ; Shahbakhti, Mahdi ( July 2019 , Proceedings of the American Control Conference)

   Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parametervarying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load. 

   more » « less
2. Data-Driven Modeling and Predictive Control of Combustion Phasing for RCCI Engines

   Khoshbakht Irdmousa, Behrouz ; Rizvi, Syed Z ; Mohammadpour Velni, Javad ; Naber, Jeffrey ; Shahbakhti, Mahdi ( July 2019 , Proceedings of the ... American Control Conference)

   Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parameter varying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load. 

   more » « less
3. Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines

   https://doi.org/10.1109/CCTA41146.2020.9206358  

   Basina, L. N. ; Irdmousa, B. K. ; Mohammadpour Velni, J. ; Borhan, H. ; Naber, J. D. ; Shahbakhti, M. ( January 2020 , IEEE Conference on Control Technology and Applications)

   Reactivity controlled compression ignition (RCCI) is a promising low temperature combustion (LTC) regime that offers lower nitrogen oxides (NOx), soot and particulate matter (PM) emissions along with higher combustion efficiency compared to conventional diesel engines. It is critical to control maximum pressure rise rate (MPRR) in RCCI engines in order to safely and efficiently operate at varying engine loads. In this paper, a data-driven modeling (DDM) approach using support vector machines (SVM) is adapted to develop a linear parameter-varying (LPV) representation of MPRR for RCCI combustion. This LPV representation is then used in the design of a model predictive controller (MPC) to control crank angle of 50% of fuel mass fraction burn (CA50) and indicated mean effective pressure (IMEP) while limiting the MPRR. The results show that the LPV-MPC control strategy can track CA50 and IMEP with mean tracking errors of 0.9 CAD and 4.7 kPa, respectively, while limiting the MPRR to the maximum allowable value of 5.8 bar/CAD. 

   more » « less
4. Input-output Data-driven Modeling and MIMO Predictive Control of an RCCI Engine Combustion

   Khoshbakht Irdmousa, Behrouz ; Naber, Jeffrey Donald ; Mohammadpour Velni, Javad ; Borhan, Hoseinali ; Shahbakhti, Mahdi ( January 2021 , IFAC Modeling, Estimation and Control Conference (MECC))

   null (Ed.)

   This study presents a data-driven identication method based on Kernelized Canonical Correlation Analysis (KCCA) approach to generate a state-space Linear Parameter-Varying (LPV) dynamic representation for the RCCI engine combustion. An LPV model is used to estimate RCCI combustion phasing (CA50) and indicated mean eective pressure (IMEP) based on fuel injection timing and quantity. The proposed data-driven method does not require prior knowledge of the plant model states and adjusts number of states to increase the accuracy of the identied state-space model. The results demonstrate that the proposed data-driven KCCA-LPV approach provides a dependable technique to establish a fast and reasonably accurate RCCI combustion model. The established model is then incorporated in a design of a constrained MIMO Model Predictive Controller (MPC) to track desired crank angle for 50% fuel burnt and IMEP at various engine conditions. The controller performance results demonstrate that the established data-driven constrained MPC combustion controller can follow desired CA50 and IMEP with less than 1.5 CAD and 37 kPa error, respectively. 

   more » « less
5. An Online Transfer Learning Approach for Identification and Predictive Control Design with Application to RCCI Engines

   Bao, Yajie ; Mohammadpour Velni, Javad ; Shahbakhti, Mahdi ( January 2020 , 2020 ASME Dynamic Systems Control Conference)

   null (Ed.)

   This paper presents a framework to refine identified artificial neural networks (ANN) based state-space linear parametervarying (LPV-SS) models with closed-loop data using online transfer learning. An LPV-SS model is assumed to be first identified offline using inputs/outputs data and a model predictive controller (MPC) designed based on this model. Using collected closed-loop batch data, the model is further refined using online transfer learning and thus the control performance is improved. Specifically, fine-tuning, a transfer learning technique, is employed to improve the model. Furthermore, the scenario where the offline identified model and the online controlled system are “similar but not identitical” is discussed. The proposed method is verified by testing on an experimentally validated high-fidelity reactivity controlled compression ignition (RCCI) engine model. The verification results show that the new online transfer learning technique combined with an adaptive MPC law improves the engine control performance to track requested engine loads and desired combustion phasing with minimum errors. 

   more » « less