数据驱动的基于管道的强大的预测控制,用于受限制的废水处理过程
IEEE transactions on cybernetics
|August 19, 2024
概括
本研究介绍了一种数据驱动的基于管道的强有力的预测控制 (DTRPC) 策略,用于废水处理过程. 它确保了稳定的溶氧度 (DOC) 追踪,尽管存在非线性和干扰.
科学领域:
- 环境工程 环境工程
- 控制系统工程 控制系统工程
- 废水处理技术 废水处理技术
背景情况:
- 废水处理过程 (WWTP) 呈现出未知的非线性和外部干扰,使溶解氧度 (DOC) 控制复杂化.
- 保持DOC在运营约束范围内对于有效的废水处理至关重要,但由于系统的不确定性而具有挑战性.
- 现有的控制策略经常与WWTP动态的固有复杂性和不可预测性相斗争.
研究的目的:
- 为WWTP中稳定的DOC跟踪提出一种新的数据驱动的基于管的强有力的预测控制 (DTRPC) 策略.
- 解决废水处理过程中固有的系统限制和外部干扰.
- 克服模拟非线性和动态复杂的WWTP的挑战.
主要方法:
- 设计了一种基于管的强大预测控制 (TRPC) 策略,使用了名义和辅助反控制器.
- 使用两个模糊的神经网络标识符来准确预测非线性WWTP动态的建模.
- 利用通用的乘法方法和梯度下降算法来优化控制规律.
主要成果:
- 在运营限制范围内,DTRPC战略成功实现了DOC的稳定跟踪控制.
- 提出的方法有效地抑制了外部干扰,并恢复了系统的名义性能.
- 在基准模型上的模拟表明了DTRPC战略的可行性,稳定性和有效性.
结论:
- 开发的DTRPC战略为复杂的WWTP中控制DOC提供了强大的数据驱动解决方案.
- 模糊神经网络和基于管的预测控制的组合提高了系统性能和约束满足.
- 这种方法提供了一种可靠的方法来管理未知的非线性和干扰的WWTP.
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