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Data-driven model predictive control of nonlinear systems using dynamic mode decomposition
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DOI：10.1080/00207721.2025.2535538.png)
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This study explores the development of a data-driven control strategy for complex nonlinear systems. Based on the stream of temporal measurements, the online dynamic mode decomposition discovers the underlying nonlinear system and transforms it to a linear time-varying realisation. The model predictive control is then designed for the discovered linear time-varying model. To address the problem of potential fluctuations in the extracted model that could lead to system divergence, tracking error is utilised as a criterion to determine whether a new model should be obtained. The efficacy of the proposed approach in governing complex nonlinear systems, such as the chaotic Lorenz system, is demonstrated. Simulation studies illustrate the high efficiency of the proposed method in both the stabilisation and regulation control of the Lorenz and Van der Pol systems.
Keyword:
Online dynamic mode decomposition
model predictive control
data-driven control
nonlinear system
chaotic system
期刊
I
IF:
4.6
论文数:
1.1K
被引数:
7.3K
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