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    Deep reinforcement learning for air handling control
    43.
    发明授权
    Deep reinforcement learning for air handling control 有权

    公开(公告)号：US11002202B2

    公开(公告)日：2021-05-11

    申请号：US16106311

    申请日：2018-08-21

    Applicant: Cummins Inc.

    Inventor： Kartavya Neema ,  Vikas Narang ,  Govindarajan Kothandaraman ,  Shashank Tamaskar

    IPC: F02D41/00 ,  F02D41/18 ,  G06N3/08 ,  F02D41/28 ,  G06N20/00

    Abstract: An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling actuators in response to receiving the control signal. In one aspect of the embodiment, the control signal is a command signal for the air handling actuators.

    DEEP REINFORCEMENT LEARNING FOR AIR HANDLING AND FUEL SYSTEM REFERENCING
    46.
    发明申请
    DEEP REINFORCEMENT LEARNING FOR AIR HANDLING AND FUEL SYSTEM REFERENCING 审中-公开

    公开(公告)号：US20200063681A1

    公开(公告)日：2020-02-27

    申请号：US16106300

    申请日：2018-08-21

    Applicant: Cummins Inc.

    Inventor： Kartavya Neema ,  Vikas Narang ,  Govindarajan Kothandaraman ,  Shashank Tamaskar

    IPC: F02D41/14 ,  F02D41/00 ,  G06N3/08 ,  G06F15/18 ,  F02D41/18 ,  F02D41/28

    Abstract: An engine system includes an air handling and fuel system whose states are managed by a reference managing unit. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The reference managing unit includes a controller which controls the air handling and fuel system of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines air handling references and fuel system references by using the policy function after receiving the current state as an input, and outputs the air handling references and fuel system references to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling and fuel system of the engine in response to receiving the air handling references and the fuel system references.

    DEEP REINFORCEMENT LEARNING FOR AIR HANDLING CONTROL
    47.
    发明申请
    DEEP REINFORCEMENT LEARNING FOR AIR HANDLING CONTROL 审中-公开

    公开(公告)号：US20200063676A1

    公开(公告)日：2020-02-27

    申请号：US16106311

    申请日：2018-08-21

    Applicant: Cummins Inc.

    Inventor： Kartavya Neema ,  Vikas Narang ,  Govindarajan Kothandaraman ,  Shashank Tamaskar

    IPC: F02D41/00 ,  G06N3/08 ,  G06F15/18 ,  F02D41/18 ,  F02D41/28

    Abstract: An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling actuators in response to receiving the control signal. In one aspect of the embodiment, the control signal is a command signal for the air handling actuators.

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