TY - JOUR
T1 - An adaptive super-twisting algorithm based on conditioning technique
AU - Liu, Dakai
AU - Esche, Sven
AU - Wang, Mingang
N1 - Publisher Copyright: © The Author(s) 2021.
PY - 2022/1
Y1 - 2022/1
N2 - This paper presents an adaptive conditioning-technique-based super-twisting algorithm aiming at improving the convergence speed and reducing the overshoot at the same time. Compared with a recently proposed method called new modified super-twisting algorithm, in which a linear acceleration factor and a damping factor are added to achieve this goal, the proposed method has several advantages. First, the proposed method enhances the convergence performance of the system by resorting to the characteristics of the conditioned super-twisting algorithm and the adaptive gains, without changing the basic structure of the classical super-twisting controller. Thus, stability proof of this method is much simpler and more concise. Furthermore, unlike the new modified super-twisting algorithm, in which an unnatural assumption on the Lipschitz disturbance is made for the stability proof, this method can counteract not only typical bounded Lipschitz disturbances but also square-root growth disturbances. Also, a set of less conservative control gains can be obtained with the proposed algorithm than with the compared algorithm. Apart from these benefits, several simulation results illustrate that the performance of the proposed method is even better in convergence and recovering from disturbance.
AB - This paper presents an adaptive conditioning-technique-based super-twisting algorithm aiming at improving the convergence speed and reducing the overshoot at the same time. Compared with a recently proposed method called new modified super-twisting algorithm, in which a linear acceleration factor and a damping factor are added to achieve this goal, the proposed method has several advantages. First, the proposed method enhances the convergence performance of the system by resorting to the characteristics of the conditioned super-twisting algorithm and the adaptive gains, without changing the basic structure of the classical super-twisting controller. Thus, stability proof of this method is much simpler and more concise. Furthermore, unlike the new modified super-twisting algorithm, in which an unnatural assumption on the Lipschitz disturbance is made for the stability proof, this method can counteract not only typical bounded Lipschitz disturbances but also square-root growth disturbances. Also, a set of less conservative control gains can be obtained with the proposed algorithm than with the compared algorithm. Apart from these benefits, several simulation results illustrate that the performance of the proposed method is even better in convergence and recovering from disturbance.
KW - Super-twisting algorithm
KW - adaptive control
KW - conditioning algorithm
KW - sliding mode control
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U2 - 10.1177/01423312211040317
DO - 10.1177/01423312211040317
M3 - Article
SN - 0142-3312
VL - 44
SP - 497
EP - 505
JO - Transactions of the Institute of Measurement and Control
JF - Transactions of the Institute of Measurement and Control
IS - 2
ER -