Hierarchical nonlinear model predictive control with multi-time-scale for wastewater treatment process

水准点(测量) 模型预测控制 过程(计算) 理论(学习稳定性) 非线性系统 控制理论(社会学) 计算机科学 控制变量 过程控制 比例(比率) 控制(管理) 控制工程 数学优化 工程类 数学 人工智能 机器学习 大地测量学 物理 操作系统 地理 量子力学
作者
Honggui Han,Shijia Fu,Haoyuan Sun,Junfei Qiao
出处
期刊:Journal of Process Control [Elsevier]
卷期号:108: 125-135 被引量:23
标识
DOI:10.1016/j.jprocont.2021.11.002
摘要

Different time scales are inevitable for the controlled variables of wastewater treatment process (WWTP), which may degrade the operation performance or even destroy the stability of the closed-loop system. In this paper, a hierarchical nonlinear model predictive control (HNMPC) strategy is developed to deal with the different time scales of controlled variables to improve the operation performance of WWTP. The main merits of HNMPC are three-fold. First, a hierarchical control structure is developed to obtain reasonable control laws. Then, the controlled variables with different time scales can be tracked by different level controllers. Second, a gradient method is developed to solve the hierarchical optimization problem of HNMPC to reduce the computational cost. Then, the fast response of HNMPC can be achieved to improve the operation performance. Third, the stability of HNMPC is proved in theory. Then, the corresponding stability conditions are given to guide the practical application. Finally, the testing results on the benchmark simulation model verify that the proposed HNMPC can achieve suitable operation performance in terms of control accuracy. • In this work, a hierarchical nonlinear model predictive control (HNMPC) strategy is developed to deal with the different time scales of control variables to improve the operation performance of wastewater treatment process. • Firstly, a hierarchical control structure is developed to obtain reasonable control laws. Then, the control variables with different time scales can be tracked by different level controllers. • Secondly, a gradient method is developed to solve the hierarchical optimization problem of HNMPC to reduce the computational cost. Then, the fast response of HNMPC can be achieved to improve the operation performance. • Thirdly, the stability of HNMPC is proved in theory. Then, the corresponding stability conditions are given to guide the practical application.
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