An adaptive distributed architecture for multi-agent state estimation and control of complex process systems

被引:2
作者
Ebrahimi, Amirmohammad [1 ]
Pourkargar, Davood B. [1 ,2 ]
机构
[1] Kansas State Univ, Tim Taylor Dept Chem Engn, Manhattan, KS 66503 USA
[2] Kansas State Univ, Food Sci Inst, Manhattan, KS 66503 USA
基金
美国国家科学基金会;
关键词
Multi-agent systems; Distributed estimation and control; Integrated process systems; Model predictive control; Moving horizon estimation; System decomposition; MODEL-PREDICTIVE CONTROL; PROCESS NETWORKS; SUBSYSTEM DECOMPOSITION;
D O I
10.1016/j.cherd.2024.09.014
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
A multi-agent integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) framework is developed for complex process networks. This framework utilizes an adaptive spectral community detection-based decomposition approach for a weighted graph representation of the state space model of the system to identify the optimal communities for distributed estimation and control. As the operating conditions of the process network change, the system decomposition adjusts, and the estimation and control agents are reassigned accordingly. These adjustments enable optimizing the integrated DMHE and DMPC architecture, enhancing robustness and closed-loop system performance. The effectiveness of the proposed adaptive distributed multi-agent estimation and control framework is demonstrated through a benchmark benzene alkylation process under various operating conditions. Simulation results show that the proposed multi-agent approach enhances closed-loop performance and computational efficiency compared to traditional system decomposition methods using unweighted hierarchical community detection.
引用
收藏
页码:594 / 604
页数:11
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