A Knowledge-based reinforcement learning control approach using deep Q network for cooling tower in HVAC systems

In order to improve the control performance of the complex mechatronic system, like the cooling tower in HVAC systems, a data-driven control approach based on model-free deep reinforcement learning method is proposed. The deep Q network (DQN) method, as one of the approaches in reinforcement learning (RL) scheme, can learn and obtain robust feedback control laws by using direct interaction data from the environment. At the same time, radial basis function neural network (RBFNN) is used for the design of deep Q network. Furthermore, the prior knowledge is used to develop an exploration strategy for the RL controller. And the strategy can guide the RL controller to explore the action space, which leads reduction on the training time as compared to traditional DQN controller. At last, the results of simulation show that the DQN controller can achieve lower Integral Absolute Error (IAE) and Integral Square Error (ISE) compared to Proportional integral (PI) controller.