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Multirate compact form dynamically linearised modelling for data-driven optimal control
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DOI：10.1080/00207721.2026.2634957.png)
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Abstract
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This paper develops a robust framework for designing accurate control for nonlinear systems with unknown dynamics and bounded disturbances. A Multirate-Compact-Form-Dynamically-Linearised (MCFDL) model is proposed to facilitate the design. The proposed design captures the system dynamics between two consecutive inputs. It provides a more precise approximation of the input-output relations by capitalising on the faster output sampling rate compared to the input application rate and dynamic linearisation (DL). The approach employs a Neural Network (NN) to model the nonlinear element in the MCFDL model, accompanied by the design of an optimal control. Rigorous analysis of the closed-loop system establishes bounds for NN weights and tracking error.
Keywords:
Dynamic linearisation
multirate sampling
robust control
data-driven control
NARMAX model
unknown nonlinear system
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4.6
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1.1K
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7.3K
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