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
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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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