Wireless Information Networking (WIN) Group, Escola d'Enginyeria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
Advanced Systems for Automation and Control (ASAC) Group, Escola d'Enginyeria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
Sensors (Basel). 2021 Sep 21;21(18):6315. doi: 10.3390/s21186315.
In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively.
在过去的十年中，工业环境的控制过程发生了变化。越来越多的控制策略采用人工神经网络（ANNs）来支持控制操作，甚至作为主要的控制结构。因此，控制结构可以直接从输入和输出测量中获得，而无需对被控过程有大量的了解。然而，ANNs 必须进行设计、实现和训练，这可能会变得复杂且耗时。通过迁移学习（TL）方法可以缓解这种情况，这些方法可以将从单个 ANN 中获得的知识转移到其余的网络中，从而减少 ANN 的设计时间。从控制的角度来看，首先可以很容易地获得第一个 ANN，然后将其转移到其余的控制回路中。在本文中，分析了将 TL 方法应用于设计和实现废水处理厂（WWTP）的控制回路。结果表明，采用这种基于 TL 的方法可以在不要求对被控过程有大量了解的情况下开发新的控制回路。此外，与传统的控制结构相比，控制性能也得到了广泛的提高。例如，结果表明，通过在跟踪期望设定点方面减少振荡，实现了综合绝对误差和综合平方误差的改进，分别从 40.17%提高到 94.29%和从 34.27%提高到 99.71%。
