A Local Direct Method for Module Identification in Dynamic Networks With Correlated Noise

• 成果类型: Article
• 署名作者: Ramaswamy, Karthik Raghavan; Van den Hof, Paul M. J.
• 署名单位: Eindhoven University of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3035634
• 发表日期: 2021
• 页码: 5237-5252
• 关键词: Network topology; MISO communication; Transfer functions; Maximum likelihood estimation; TOPOLOGY; MIMO communication; correlation; closed-loop identification; Correlated noise; dynamic networks; predictor input and predicted output selection; System identification

摘要

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in an multiple-input-single-output (MISO) identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in an multiple-input--multiple-output (MIMO) identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is a set of algorithms that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in an MISO or MIMO identification setup. Three algorithms are presented that differ in their approach of selecting measured node signals. Either a maximum or a minimum number of measured node signals can be considered, as well as a preselected set of measured nodes.