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An Improved Approach for Robust MPC Tuning Based on Machine Learning
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2021-05-15 , DOI: 10.1155/2021/5518950
Ning He 1 , Mengrui Zhang 1 , Ruoxia Li 2
Affiliation  

A robust tuning method based on an artificial neural network for model predictive control (MPC) of industrial systems with parametric uncertainties is put forward in this work. Firstly, an efficient approach to characterize the mapping relationship between the controller parameters and the robust performance indices is established. As there are normally multiple conflicted robust performance indices to be considered in MPC tuning, the neural network is further used to fuse the indices to produce a simple label representing the acceptable level of the robust performance. Finally, an automated algorithm is proposed to tune the MPC parameters for the considered uncertain system to achieve the desired robust performance. In addition, the regulation of the pH value of the sewage treatment system is used to verify the effectiveness of the robust tuning algorithm which is described in this paper.

中文翻译:

一种基于机器学习的鲁棒MPC调整的改进方法

提出了一种基于人工神经网络的鲁棒调整方法,用于参数不确定性工业系统的模型预测控制(MPC)。首先,建立了一种表征控制器参数与鲁棒性能指标之间映射关系的有效方法。由于在MPC调整中通常要考虑多个有冲突的鲁棒性能指标,因此神经网络将进一步用于融合这些指标,以生成代表鲁棒性能可接受水平的简单标签。最后,提出了一种自动化算法来为所考虑的不确定系统调整MPC参数,以实现所需的鲁棒性能。此外,
更新日期:2021-05-15
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