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• W3203806719 abstract "This paper is concerned with the disturbance estimation and velocity recovery of autonomous surface ve hicles subject to unknown input gains, in addition to lumped uncertainties composed of unknown internal dynamics and external disturbances. Existing extended state observer design methods are not applicable due to the fact that some prior knowledge on model parameters such as the control input gains are assumed to be known in advance. By incorporating a concurrent learning approach into the extended state observer design, a three-order data-driven adaptive extended state observer based on position and acceleration information is proposed to estimate the unknown input gains, lumped disturbance, and unmeasured velocities. An advantage of the proposed data-driven adaptive extended state observer is that the unknown input gains, lumped disturbance, and unknown velocities can be simultaneously estimated with guaranteed convergence thanks to concurrent learning. The result is extended to a data-driven adaptive extended state observer with finite-time convergence. The efficacy of the proposed two data-driven adaptive extended state observers for autonomous surface vehicles is substantiated via simulations." @default.
• W3203806719 created "2021-10-11" @default.
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• W3203806719 date "2022-01-01" @default.
• W3203806719 modified "2023-10-14" @default.
• W3203806719 title "Data-driven adaptive extended state observer design for autonomous surface vehicles with unknown input gains based on concurrent learning" @default.
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• W3203806719 doi "https://doi.org/10.1016/j.neucom.2021.09.062" @default.
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