A Gain Adaptive Strategy to Improve Closed-loop Performance of Robust Asymptotic Feedback Linearization

This contribution presents a gain adaptation, which allows us to tune a robust asymptotic feedback linearization (RAFL). The gain adaptation allows the RAFL to attenuate the measurement noise sensitivity. The RAFL is considered here because it ensures tracking without prior information about the sys...

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Bibliographic Details
Published in:International journal of control, automation, and systems automation, and systems, 2023-09, Vol.21 (9), p.2957-2968
Main Authors: Solís-Perales, Gualberto, Sánchez-Estrada, Jairo, Femat, Ricardo
Format: Article
Language:English
Subjects:
Adaptation
Adaptive control
Amplification
Anaerobic digestion
Anaerobic processes
Asymptotic properties
Chemical oxygen demand
Closed loops
Control
D C motors
Engineering
Errors
Feedback linearization
High gain
Mathematical models
Mechatronics
Noise measurement
Noise sensitivity
Nonlinearity
Perturbation
Regular Papers
Robotics
Robust control
Tracking problem
Uncertainty
Citations: Items that this one cites
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Summary:This contribution presents a gain adaptation, which allows us to tune a robust asymptotic feedback linearization (RAFL). The gain adaptation allows the RAFL to attenuate the measurement noise sensitivity. The RAFL is considered here because it ensures tracking without prior information about the system’s nonlinearities and parameter bounds. Also, the RAFL only has the system output available for feedback. In this work, the robust tracking problem is faced considering: modeling errors, parametric variations, external perturbations, and noisy output measurement. On one side, the RAFL control faces modeling errors, parametric variations, and external perturbations through an observer that estimates uncertainties using an extra state, which lumps all the unknown nonlinearities and uncertainties. On the other hand, the proposed adaptive gain function allows the observer’s high gain to vary to have a fast observer’s convergence while simultaneously avoiding amplifying the measurement noise in the steady-state. The adaptive gain function provides the RAFL control robustness against noisy measurement. Thereby, the RAFL control with adaptive gain function becomes a robust feedback linearizing against to measurement noise. Finally, the RAFL controller with the adaptive gain function is illustrated by a numerical simulation of a tracking problem for a DC-motor and a chemical oxygen demand regulation in an anaerobic digestion process.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-0858-6