Subspace identification of dynamic processes with consideration of time delays: A Bayesian optimization scheme

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Qingyuan Liu , Tao Liu , Dexian Huang , Chao Shang
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引用次数: 0

Abstract

For decades, subspace identification method (SIM) has been widely adopted for modeling multiple-input multiple-output processes. However, conventional SIMs yield unsatisfactory performance in modeling processes with evident dead time characteristics. To tackle this challenge, we develop in this work an efficient SIM scheme with consideration of time delays along with a tailored Bayesian optimization (BO) solution algorithm, aiming at simultaneously identifying the state-space matrices, time delays and model order of a time-delayed state-space model from input–output data. The identification problem is formulated as a black-box optimization problem over time delays and model order. In the proposed tailored BO algorithm, a decomposition strategy is developed to address the existence of multiple identical solutions. Besides, a prior-weighted acquisition function is proposed to improve the algorithm efficiency. Numerical examples and an experiment on industrial dataset showcase that the proposed method achieves significant improvement in identification accuracy over conventional SIMs owing to the explicit consideration of time delays. In addition, the proposed BO algorithm outperforms the naive random search and the naive BO algorithm in terms of computational efficiency.
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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