Model-free adaptive mixed water temperature control system and method based on deep reinforcement learning

A model-free self-adaptive and reinforcement learning technology, applied in self-adaptive control, general control system, control/regulation system, etc., can solve problems such as wasting water resources and difficult temperature regulation, and achieve strong adaptability and reliable mixed water system And accurate, avoid the effect of frequent temperature changes

Active Publication Date: 2022-08-09
HARBIN UNIV OF COMMERCE
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Problems solved by technology

[0006] The invention solves the problems of difficulty in temperature adjustment and waste of water resources in the current manual temperature adjustment of the existing mixing water device. The invention discloses a "model-free adaptive mixing water temperature control system and method based on deep reinforcement learning"

Method used

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  • Model-free adaptive mixed water temperature control system and method based on deep reinforcement learning
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  • Model-free adaptive mixed water temperature control system and method based on deep reinforcement learning

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specific Embodiment approach 1

[0046] Embodiment 1: Combining Figure 1-Figure 3Describe this embodiment. The model-free adaptive mixed water temperature control system based on deep reinforcement learning in this embodiment includes an action network module and a value network module;

[0047] The action network module includes an estimation network module and an evaluation target network module;

[0048] The action network module is used to define the state space and action space of the mixed water system;

[0049] The value network module is used to judge and evaluate the network environment;

[0050] The action network module and the value network module are used to interact with the environment to obtain the DDPG model.

specific Embodiment approach 2

[0051] Specific implementation mode 2: Combining Figure 1-Figure 3 Describe this embodiment, the model-free adaptive temperature control method for mixed water based on deep reinforcement learning in this embodiment, the specific method steps are as follows:

[0052] Step 1: Customize the state space and action space of the mixed water system, and establish an action network and a value network;

[0053] In step 2, the action network and the value network are trained according to the data generated by interacting with the mixed water environment, and the mixed water temperature regulation DDPG model is obtained;

[0054] In step 3, the DDPG model is deployed in the mixed water equipment, and communicates with the cloud server in real time to asynchronously update the parameters of the equipment model, so as to realize adaptive learning of the new mixed water environment.

specific Embodiment approach 3

[0055] Specific implementation three: combination Figure 1-Figure 3 This embodiment is described. In the model-free adaptive mixed water temperature control method based on deep reinforcement learning in this embodiment, in step 1, the action network includes: an action network and a target action network; the value network includes a judgment value network , the state space and action space of the target value network mixed water system, the action space of the mixed water system is the rotation speed of the adjustment paddle A∈[V max , V min ], where V max is the maximum speed of temperature regulation, V min =-V max ;

[0056] The state space S is specifically: Which represent: temperature at the cold water end before mixing, pressure at the cold end before mixing, water flow at the cold end before mixing, temperature at the hot water end before mixing, pressure at the hot water end before mixing, and water flow at the hot water end before mixing , the current tempe...

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Abstract

A model-free adaptive mixed water temperature control system and method based on deep reinforcement learning belong to the field of cold and hot water mixed water temperature control. The invention solves the problems of manual temperature adjustment of the existing water mixing device, difficulty in temperature adjustment, waste of water resources, and the like. The present invention includes an action network module and a value network module. The specific method steps of the present invention are as follows: step 1, define the state space and action space of the mixed water system, and establish an action network and a value network; step 2, according to the interaction with the mixed water environment The generated data trains the action network and the value network, and obtains the DDPG model of mixed water temperature regulation; step 3, deploys the DDPG model in the mixed water equipment, communicates with the cloud server in real time, asynchronously updates the parameters of the equipment model, and realizes adaptive learning of new mixed water surroundings. The temperature control system and method of the present invention can adapt to the use environment, have strong adaptability to environmental factors, and make the water mixing system reliable and accurate.

Description

technical field [0001] The invention relates to a model-free self-adaptive mixed water temperature control system and method based on deep reinforcement learning, and belongs to the field of cold and hot water mixed water temperature control. Background technique [0002] Most of the traditional water mixing devices use manual temperature adjustment, which has problems such as difficulty in temperature adjustment and waste of water resources. However, some intelligent water mixing thermostat systems on the market usually use fixed algorithms, and the problem is that they cannot be adapted. Different environments have problems such as poor reliability and poor accuracy. [0003] Most of the existing control research work is on single-variable PID control, and its theory and design are well established, understood, and practically applied. But the entire multivariable PID system has not been successful, and most industrial processes are multivariable in nature. [0004] The ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 黄文俊兰琦琦解泽宇
Owner HARBIN UNIV OF COMMERCE
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