使用深度强化学习来控制循环冷却水系统的自适应控制
Jin Xu1, Han Li1, Qingxin Zhang1
1School of Artificial Intelligence, Shenyang Aerospace University, Liaoning, China.
PloS one
|July 24, 2024
概括
一个新的自适应控制结构,TD3-RTM,增强循环冷却水系统的稳定性. 这种深度强化学习方法显著减少过渡时间和错误,优于传统方法.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 水系统管理 水系统管理
背景情况:
- 循环冷却水系统面临复杂的控制挑战.
- 传统的控制方法往往无法实现稳定和精确的系统调节.
- 这些系统的多变量性质需要先进的控制策略.
研究的目的:
- 引入一种新的自适应控制结构,TD3-RTM,以改善循环冷却水系统的控制.
- 为了提高系统稳定性和精度,利用深度强化学习.
- 为了减少控制系统中的振荡和不稳定性,使用参考轨迹模型.
主要方法:
- 开发了一种使用双延迟深度决定性政策梯度 (TD3) 算法的深度强化学习代理.
- 将参考轨迹模型 (RTM) 纳入TD3算法,创建TD3-RTM.
- 定义了一个状态空间,并设计了一个根据系统的多变量特征量身定制的密集奖励函数.
- 在 MATLAB/Simulink 中进行模拟实验以评估性能.
主要成果:
- 与PID,模糊PID,DDPG和TD3.3相比,TD3-RTM在流量和温度循环中显著改善了过渡时间.
- 在所有测试的循环中,绝对误差积分 (IAE) 显著减少.
- 与PID,模糊PID和TD3.3相比,流环中的超射明显减少.
结论:
- TD3-RTM方法在稳定循环冷却水系统方面提供了卓越的性能.
- 这种适应性控制结构有效地解决了传统控制方法的局限性.
- TD3-RTM展示了优化复杂工业用水系统运行的潜力.
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