A New Varying-Gain-Exponent-Based Differentiator/Observer: An Efficient Balance Between Linear and Sliding-Mode Algorithms
Por:
Ghanes, Malek, Barbot, Jean-Pierre, FRIDMAN, LEONID, Levant, Arie, Boisliveau, Robert
Publicada:
1 dic 2020
Resumen:
It is well known that the supertwisting algorithm is robust to matched
perturbation but is sensitive to measurement noise. Contrary to this,
the classical linear algorithm is less sensitive to measurement noise
but less robust to perturbation. To combine both the good accuracy of
the supertwisting algorithm with respect to perturbation and the good
performance of the linear algorithm with respect to measurement noise,
this article proposes a new differentiator/observer with a varying
exponent gain alpha whose variation depends on the magnitude of
measurement noise (high-frequency signal). When the magnitude of
measurement noise increases (respectively, decreases) alpha tends to 1
(respectively, tends to 0.5) and the proposed differentiator/observer
behaves as a linear algorithm (respectively, as a supertwisting
algorithm). Thus, by one parameter alpha, the differentiator/observer
can take care of high-frequency noise and matched perturbations. A
complete stability analysis of the proposed differentiator/observer is
provided. To highlight the applicability of the proposed methodology,
the dedicated differentiator/observer is, respectively, validated on the
electropneumatic actuator and electric machine test benches. These
experimental results are compared to those of linear and supertwisting
algorithms.
Filiaciones:
Ghanes, Malek:
CNRS, LS2N, Cent Nantes, F-44321 Nantes, France
Barbot, Jean-Pierre:
ENSEA, Quartz, F-95014 Cergy Pontoise, France
CNRS, LS2N, Nantes, France
FRIDMAN, LEONID:
Univ Nacl Autonoma Mexico, Mexico City 04510, DF, Mexico
Levant, Arie:
Tel Aviv Univ, IL-6997801 Tel Aviv, Israel
Boisliveau, Robert:
CNRS, LS2N, Nantes, France
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