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- Title
Adaptive gains of dual level to super-twisting algorithm for sliding mode design.
- Authors
Dong Luo; Xiaogang Xiong; Shanghai Jin; Kamal, Shyam
- Abstract
A gain-adaption mechanism of a dual level to the super-twisting algorithm (STA) for adaptive sliding mode design is studied. The proposed dual level method first tunes a third-order sliding mode observer to exactly estimate the magnitude level of external disturbances, and then adjusts the two gains (α, β) of STA online simultaneously such that a second-order sliding mode can take place with small rectifying gains. The gains of the third-order sliding mode observer are adjusted by exploring the homogeneous property such that only one auxiliary parameter L is needed to be tuned. The magnitude of this parameter L increases until the error between the observer output and actual disturbance disappears. While driving the sliding variable to the sliding mode surface of STA, one gain β of the STA automatically converges to an adjacent area of the perturbation magnitude in finite time. The other gain α is adjusted by the gain β to guarantee the robustness of the STA. This method requires no intervention during adaptation. The usefulness is illustrated by an example of designing an equivalent control-based sliding mode control with the proposed adaptive STA for a perturbed linear time-invariant system.
- Subjects
SLIDING mode control; LYAPUNOV functions; LOWPASS electric filters; DIFFERENTIAL equations; ARTIFICIAL neural networks
- Publication
IET Control Theory & Applications (Wiley-Blackwell), 2018, Vol 12, Issue 17, p2347
- ISSN
1751-8644
- Publication type
Article
- DOI
10.1049/iet-cta.2018.5380