171 research outputs found
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
The process of developing control functions for embedded systems is
resource-, time-, and data-intensive, often resulting in sub-optimal cost and
solutions approaches. Reinforcement Learning (RL) has great potential for
autonomously training agents to perform complex control tasks with minimal
human intervention. Due to costly data generation and safety constraints,
however, its application is mostly limited to purely simulated domains. To use
RL effectively in embedded system function development, the generated agents
must be able to handle real-world applications. In this context, this work
focuses on accelerating the training process of RL agents by combining Transfer
Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient
exhaust gas re-circulation control for an internal combustion engine, use of a
computationally cheap Model-in-the-Loop (MiL) simulation is made to select a
suitable algorithm, fine-tune hyperparameters, and finally train candidate
agents for the transfer. These pre-trained RL agents are then fine-tuned in a
Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for
adjusting the reward parameters when advancing to real hardware. Further, the
comparison between a purely HiL-trained and a transferred agent showed a
reduction of training time by a factor of 5.9. The results emphasize the
necessity to train RL agents with real hardware, and demonstrate that the
maturity of the transferred policies affects both training time and
performance, highlighting the strong synergies between TL and XiL simulation
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
Steuergerät und Verfahren zur Steuerung eines Betriebspunktes eines hybriden Antriebssystems
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