Abstract
Deep reinforcement learning (DRL) is an area of machine learning that combines a deep learning approach and reinforcement learning (RL). However, there seem to be few studies that analyze the latest DRL algorithms on real-world powertrain control problems. Meanwhile, the boost control of a variable geometry turbocharger (VGT)-equipped diesel engine is difficult mainly due to its strong coupling with an exhaust gas recirculation (EGR) system and large lag, resulting from time delay and hysteresis between the input and output dynamics of the engine’s gas exchange system. In this context, one of the latest model-free DRL algorithms, the deep deterministic policy gradient (DDPG) algorithm, was built in this paper to develop and finally form a strategy to track the target boost pressure under transient driving cycles. Using a fine-tuned proportion integration differentiation (PID) controller as a benchmark, the results show that the control performance based on the proposed DDPG algorithm can achieve a good transient control performance from scratch by autonomously learning the interaction with the environment, without relying on model supervision or complete environment models. In addition, the proposed strategy is able to adapt to the changing environment and hardware aging over time by adaptively tuning the algorithm in a self-learning manner on-line, making it attractive to real plant control problems whose system consistency may not be strictly guaranteed and whose environment may change over time.
Highlights
The concept of engine downsizing and down-speeding enables reductions in fuel consumption and CO2 emissions from passenger cars in order to satisfy the greenhouse gas emission reduction targets set by the 2015 Paris Climate Change Conference [1,2]
In order to validate its performance, the proposed deep deterministic policy gradient (DDPG) algorithm was compared to a finetuned gain scheduled proportion integration differentiation (PID) controller with both its P and I gains mapped as a function of speed and requested load
In order to validate its performance, the proposed DDPG algorithm was compared to a fine-tuned gain scheduled PID controller with both its P and I gains mapped as a function of speed and requested load
Summary
The concept of engine downsizing and down-speeding enables reductions in fuel consumption and CO2 emissions from passenger cars in order to satisfy the greenhouse gas emission reduction targets set by the 2015 Paris Climate Change Conference [1,2]. These reductions are achieved by reducing pumping and friction losses at part-load operation. The transient response of such engines is, affected by the static and dynamic characteristics of the fixed-geometry turbo-machinery (especially when it is optimized for high-end torque) [4,5]. In engines equipped with VGT, and because part of the exhaust
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