9 research outputs found
Deep Learning based Model Predictive Control for Compression Ignition Engines
Machine learning (ML) and a nonlinear model predictive controller (NMPC) are
used in this paper to minimize the emissions and fuel consumption of a
compression ignition engine. In this work machine learning is applied in two
methods. In the first application, ML is used to identify a model for
implementation in model predictive control optimization problems. In the second
application, ML is used as a replacement of the NMPC where the ML controller
learns the optimal control action by imitating or mimicking the behavior of the
model predictive controller. In this study, a deep recurrent neural network
including long-short term memory (LSTM) layers are used to model the emissions
and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine.
This model is then used for model predictive controller implementation. Then, a
deep learning scheme is deployed to clone the behavior of the developed
controller. In the LSTM integration, a novel scheme is used by augmenting
hidden and cell states of the network in an NMPC optimization problem. The
developed LSTM-NMPC and the imitative NMPC are compared with the Cummins
calibrated Engine Control Unit (ECU) model in an experimentally validated
engine simulation platform. Results show a significant reduction in Nitrogen
Oxides (\nox) emissions and a slight decrease in the injected fuel quantity
while maintaining the same load. In addition, the imitative NMPC has a similar
performance as the NMPC but with a two orders of magnitude reduction of the
computation time.Comment: Submitted to Control engineering Practice (Submission date: March 9,
2022) Revised version (Submission date: June 18, 2022) Accepted on July 30,
202
Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines
The high thermal efficiency and reliability of the compression-ignition
engine makes it the first choice for many applications. For this to continue, a
reduction of the pollutant emissions is needed. One solution is the use of
machine learning (ML) and model predictive control (MPC) to minimize emissions
and fuel consumption, without adding substantial computational cost to the
engine controller. ML is developed in this paper for both modeling engine
performance and emissions and for imitating the behaviour of an Linear
Parameter Varying (LPV) MPC. Using a support vector machine-based linear
parameter varying model of the engine performance and emissions, a model
predictive controller is implemented for a 4.5 Cummins diesel engine. This
online optimized MPC solution offers advantages in minimizing the
\nox~emissions and fuel consumption compared to the baseline feedforward
production controller. To reduce the computational cost of this MPC, a deep
learning scheme is designed to mimic the behavior of the developed controller.
The performance in reducing NOx emissions at a constant load by the imitative
controller is similar to that of the online optimized MPC compared to the
Cummins production controller. In addition, the imitative controller requires
50 times less computation time compared to that of the online MPC optimization.Comment: Submitted to Advances in Automotive Control - 10th AAC 202
End-to-End Deep Neural Network Based Nonlinear Model Predictive Control: Experimental Implementation on Diesel Engine Emission Control
In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of a 4.5-liter 4-cylinder Cummins diesel engine are modeled using a DNN model with seven hidden layers and 24,148 learnable parameters created by stacking six Fully Connected layers with one long-short term memory (LSTM) layer. This model is then implemented as the plant model in an NMPC. For real-time implementation of the LSTM-NMPC, an open-source package acados with the quadratic programming solver HPIPM (High-Performance Interior-Point Method) is employed. This helps LSTM-NMPC run in real time with an average turnaround time of 62.3 milliseconds. For real-time controller prototyping, a dSPACE MicroAutoBox II rapid prototyping system is used. A Field-Programmable Gate Array is employed to calculate the in-cylinder pressure-based combustion metrics online in real time. The developed controller was tested for both step and smooth load reference changes, which showed accurate tracking performance while enforcing all input and output constraints. To assess the robustness of the controller to data outside the training region, the engine speed is varied from 1200 rpm to 1800 rpm. The experimental results illustrate accurate tracking and disturbance rejection for the out-of-training data region. At 5 bar indicated mean effective pressure and a speed of 1200 rpm, the comparison between the Cummins production controller and the proposed LSTM-NMPC showed a 7.9% fuel consumption reduction, while also decreasing both nitrogen oxides (NOx) and Particle Matter (PM) by up to 18.9% and 40.8%
End-to-End Deep Neural Network Based Nonlinear Model Predictive Control: Experimental Implementation on Diesel Engine Emission Control
In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of a 4.5-liter 4-cylinder Cummins diesel engine are modeled using a DNN model with seven hidden layers and 24,148 learnable parameters created by stacking six Fully Connected layers with one long-short term memory (LSTM) layer. This model is then implemented as the plant model in an NMPC. For real-time implementation of the LSTM-NMPC, an open-source package acados with the quadratic programming solver HPIPM (High-Performance Interior-Point Method) is employed. This helps LSTM-NMPC run in real time with an average turnaround time of 62.3 milliseconds. For real-time controller prototyping, a dSPACE MicroAutoBox II rapid prototyping system is used. A Field-Programmable Gate Array is employed to calculate the in-cylinder pressure-based combustion metrics online in real time. The developed controller was tested for both step and smooth load reference changes, which showed accurate tracking performance while enforcing all input and output constraints. To assess the robustness of the controller to data outside the training region, the engine speed is varied from 1200 rpm to 1800 rpm. The experimental results illustrate accurate tracking and disturbance rejection for the out-of-training data region. At 5 bar indicated mean effective pressure and a speed of 1200 rpm, the comparison between the Cummins production controller and the proposed LSTM-NMPC showed a 7.9% fuel consumption reduction, while also decreasing both nitrogen oxides (NOx) and Particle Matter (PM) by up to 18.9% and 40.8%