Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotiv...Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.展开更多
Digital twin self-evolution means that digital twin can fuse online sensor data from physical entity to evolve itself,hence improve credibility of the model and represent the physical entity faithfully.There is an urg...Digital twin self-evolution means that digital twin can fuse online sensor data from physical entity to evolve itself,hence improve credibility of the model and represent the physical entity faithfully.There is an urgent need to address the fundamental theories and techniques on how to build such a self-evolving digital twin system for complex equipment optimization and control.Focused on this problem,integrating Bayesian optimization theory and deep reinforcement learning(DRL),this paper proposes a method to build dynamic self-evolving equipment digital twin system for optimal control.First,considering the complexity of current equipment and real-time requirement of dynamic self-evolution scenario,we design digital twin dynamic self-evolution engine using Bayesian optimization theory,which can continuously integrate real-time sensing data,adapt to the dynamic uncertainty changes of physical equipment,so as to improve the credibility of digital twin.Then,a decision-making agent based on DRL algorithm soft actor-critic is designed,which can interact with equipment digital twin in virtual space.When the digital twin model evolves,the agent follows and continues to learn and update itself through online fine-tuning strategy,so as to continuously improve the equipment optimization control performance.Finally,the feasibility and effectiveness of the proposed method are verified by two simulation cases.展开更多
基金Project supported by the Centre for Smart Grid and Information Convergence(CeSGIC)at Xi’an Jiaotong-Liverpool University,China
文摘Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.
基金supported by the National Natural Science Foundation of China under Grant No.62373026。
文摘Digital twin self-evolution means that digital twin can fuse online sensor data from physical entity to evolve itself,hence improve credibility of the model and represent the physical entity faithfully.There is an urgent need to address the fundamental theories and techniques on how to build such a self-evolving digital twin system for complex equipment optimization and control.Focused on this problem,integrating Bayesian optimization theory and deep reinforcement learning(DRL),this paper proposes a method to build dynamic self-evolving equipment digital twin system for optimal control.First,considering the complexity of current equipment and real-time requirement of dynamic self-evolution scenario,we design digital twin dynamic self-evolution engine using Bayesian optimization theory,which can continuously integrate real-time sensing data,adapt to the dynamic uncertainty changes of physical equipment,so as to improve the credibility of digital twin.Then,a decision-making agent based on DRL algorithm soft actor-critic is designed,which can interact with equipment digital twin in virtual space.When the digital twin model evolves,the agent follows and continues to learn and update itself through online fine-tuning strategy,so as to continuously improve the equipment optimization control performance.Finally,the feasibility and effectiveness of the proposed method are verified by two simulation cases.
