Optimal Error Quantification and Robust Tracking under Unknown Upper Bounds on Uncertainties and Biased External Disturbance

Victor F. Sokolov ()
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Victor F. Sokolov: Institute of Physics and Mathematics, Federal Research Center Komi Science Center, Ural Branch, RAS, 167982 Syktyvkar, Russia

Mathematics, 2024, vol. 12, issue 2, 1-14

Abstract: This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 𝓁 1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations.

Keywords: robust tracking; error quantification; model evaluation; optimal control; adaptive control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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