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A data‐driven sensor fault‐tolerant control scheme based on subspace identification
Author(s) -
Salim Mina,
Ahmed Saeed,
Khosrowjerdi Mohammad Javad
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5666
Subject(s) - subspace topology , control theory (sociology) , fault tolerance , fault (geology) , scheme (mathematics) , compensation (psychology) , computer science , identification (biology) , minimax , fault detection and isolation , control (management) , control engineering , engineering , mathematical optimization , mathematics , actuator , distributed computing , artificial intelligence , psychology , botany , psychoanalysis , biology , geology , mathematical analysis , seismology
We study the sensor fault estimation and accommodation problems in a data‐drivenℋ ∞setting, leading to a data‐driven sensor fault‐tolerant control scheme. First, we formulate the fault estimation problem as a finite‐horizon minimaxℋ ∞‐optimization problem in a data‐driven setup, whose solution yields the fault estimate. The estimated fault is then used for output compensation. This compensated output and the experimental input are used to achieve certain control objectives in a data‐drivenℋ ∞setting. Next, the data‐drivenℋ ∞fault estimation and control problems are solved using a subspace predictor‐based approach. Finally, the proposed algorithm is applied to the steering subsystem of the remotely operated underwater vehicle.