Adaptive control for circulating cooling water system using deep reinforcement learning

Abstract

Due to the complex internal working process of circulating cooling water systems, most traditional control methods struggle to achieve stable and precise control. Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). The structure is based on the Markov decision process of the recirculating cooling water system. Initially, the TD3 algorithm is employed to construct a deep reinforcement learning agent. Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. The agent updates its network based on different reward values obtained through interactions with the system, thereby gradually aligning the action values with the optimal policy. The TD3-RTM method introduces a reference trajectory model to accelerate the convergence speed of the agent and reduce oscillations and instability in the control system. Subsequently, simulation experiments were conducted in MATLAB/Simulink. The results show that compared to PID, fuzzy PID, DDPG and TD3, the TD3-RTM method improved the transient time in the flow loop by 6.09s, 5.29s, 0.57s, and 0.77s, respectively, and the Integral of Absolute Error(IAE) indexes decreased by 710.54, 335.1, 135.97, and 89.96, respectively, and the transient time in the temperature loop improved by 25.84s, 13.65s, 15.05s, and 0.81s, and the IAE metrics were reduced by 143.9, 59.13, 31.79, and 1.77, respectively. In addition, the overshooting of the TD3-RTM method in the flow loop was reduced by 17.64, 7.79, and 1.29 per cent, respectively, in comparison with the PID, the fuzzy PID, and the TD3.