Data-driven Disturbance Decoupling: Theory and Applications

Recent results on data-driven disturbance decoupling problem (D4P) are applied to two problems arising in practice, namely, the quadruple pendulum setup used in gravitational wave detectors in Laser Interferometer Gravitational-wave Observatory (LIGO) and the four-tank system. The first example is a quadruple pendulum setup used in LIGO. It has eight state variables, four control inputs, a single disturbance input and a single output. The second example is a linearized four tank system which has four state variables, two control inputs, and two outputs. Here disturbance input is considered to affect tanks 2, 3 and 4. For both examples, our recent D4P results are applied to compute the largest control invariant subspace in the kernel of output matrix and a corresponding decoupling feedback matrix by using the data of input, initial state and the noisy output trajectory. Computation is performed in a completely data driven manner and no model parameters are estimated at any stage. Simulation results showing disturbance decoupling are presented for both examples.

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Publication Details

Journal
IFAC-PapersOnLine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ifacol.2025.09.564
Primary Topic
Control Systems and Identification
Type
article
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0.00
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article

Data-driven Disturbance Decoupling: Theory and Applications

Debasattam Pal, Deepak U. Patil, Naveen Mukesh N
IFAC-PapersOnLine
Control Systems and Identification
article

Data-driven Disturbance Decoupling: Theory and Applications

Debasattam Pal, Deepak U. Patil, Naveen Mukesh N
article en

Abstract

Recent results on data-driven disturbance decoupling problem (D4P) are applied to two problems arising in practice, namely, the quadruple pendulum setup used in gravitational wave detectors in Laser Interferometer Gravitational-wave Observatory (LIGO) and the four-tank system. The first example is a quadruple pendulum setup used in LIGO. It has eight state variables, four control inputs, a single disturbance input and a single output. The second example is a linearized four tank system which has four state variables, two control inputs, and two outputs. Here disturbance input is considered to affect tanks 2, 3 and 4. For both examples, our recent D4P results are applied to compute the largest control invariant subspace in the kernel of output matrix and a corresponding decoupling feedback matrix by using the data of input, initial state and the noisy output trajectory. Computation is performed in a completely data driven manner and no model parameters are estimated at any stage. Simulation results showing disturbance decoupling are presented for both examples.

IFAC-PapersOnLineVol. 59(12)
Indian Institute of Technology Bombay (IN), Indian Institute of Technology Delhi (IN)
Openalex Percentile: Top 65%
Control Systems and Identification
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Data-driven Disturbance Decoupling: Theory and Applications — Debasattam Pal, Deepak U. Patil, et al. · IFAC-PapersOnLine (2025) | TGRS Research Map | TGRS