Verstärkungslernen (RL) and Machine Learning Integrated Model Predictive
Control (ML-MPC) are promising approaches for optimizing hydrogen-diesel
dual-fuel engine control, as they can effectively control multiple-input
multiple-output systems and nonlinear processes. ML-MPC is advantageous for
providing safe and optimal controls, ensuring the engine operates within
predefined safety limits. In contrast, RL is distinguished by its adaptability
to changing conditions through its learning-based approach. Jedoch, the
practical implementation of either method alone poses challenges. RL requires
high variance in control inputs during early learning phases, which can pose
risks to the system by potentially executing unsafe actions, leading to
mechanical damage. Conversely, ML-MPC relies on an accurate system model to
generate optimal control inputs and has limited adaptability to system drifts,
such as injector aging, which naturally occur in engine applications. To
address these limitations, this study proposes a hybrid RL and ML-MPC approach
that uses an ML-MPC framework while incorporating an RL agent to dynamically
adjust the ML-MPC load tracking reference in response to changes in the
environment. Gleichzeitig, the ML-MPC ensures that actions stay safe
throughout the RL agent’s exploration. To evaluate the effectiveness of this
Ansatz, fuel pressure is deliberately varied to introduce a model-plant
mismatch between the ML-MPC and the engine test bench. The result of this
mismatch is a root mean square error (RMSE) in indicated mean effective
pressure of 0.57 bar when running the ML-MPC. The experimental results
demonstrate that RL successfully adapts to changing boundary conditions by
altering the tracking reference while ML-MPC ensures safe control inputs. Der
quantitative improvement in load tracking by implementing RL is an RSME of 0.44
bar.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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