Adaptive Second-Order Sliding Mode Control: A Lyapunov Approach

Ding, Shihong; Mei, Keqi; Yu, Xinghuo

doi:10.1109/tac.2021.3115447

Cited by 69 publications

(25 citation statements)

References 27 publications

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“…Frequently, the unexpected chattering will result in hazardous oscillation to dramatically degrade system performance [10]. Fortunately, the second-order sliding-mode (SOSM) algorithms (see, e.g., the twisting method [11] and the suboptimal algorithm [12]) can mitigate the adverse chattering effect with maintaining the robustness and accuracy of conventional sliding mode (SM) [13]- [17]. Among the existing SOSM algorithms, the supertwisting algorithm (STA) has played a special role and acquired widespread interest owing to its three merits.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Supertwisting Algorithm

Mei

Ding

2023

IEEE Trans. Cybern.

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The work proposes a generalized supertwisting algorithm (GSTA) and its constructive design strategy. In contrast with the conventional STA, the most remarkable characteristic of the proposed method is that the discontinuous term in the conventional STA is replaced with a fractional power term, which can fundamentally improve the performance of the conventional STA. It is shown that if the fractional power in the nonsmooth term becomes −1/2, the GSTA will reduce to the conventional STA. Under the GSTA, it will be rigorously verified by taking advantage of strict Lyapunov analysis that the sliding variables can finite-time converge to an arbitrarily small region in a neighborhood of the origin by tuning the gains and the fractional power. Finally, simulation studies are provided to demonstrate the superiority of the theoretically obtained results.

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Section: Introductionmentioning

confidence: 99%

A Generalized Supertwisting Algorithm

Mei

Ding

2023

IEEE Trans. Cybern.

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“…On the other hand, as a result of the assorted communication constraints, such as, limited bandwidth, data dropout, and random fading, the networked structure may encounter data loss, which can be found in some literature, for example, References 33‐40. Compared with other kinds of data loss, the random fading phenomenon is frequently ignored in spite of its general existence in network communication.…”

Section: Introductionmentioning

confidence: 99%

Passive state estimation for Markov jumping inertial neural networks under fading channels

Wang

Qin

Fang

et al. 2022

Adaptive Control & Signal

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SummaryThis article studies the issue of passive state estimation for inertial neural networks with Markov jumping parameters and fading channels. To facilitate the state estimator design, inertial neural networks which is initially described by the second‐order form, is converted into a first‐order one via a suitable variable substitution. Additionally, the channel fading problem, which is caused by the long‐distance propagation of the signal in the network environment, is described by Bernoulli sequences. Then, some sufficient criteria are established in view of the Lyapunov theory, which can guarantee that the estimation error system is stochastically stable and achieves the desired passive performance. A solution of the state estimator design problem for Markov jumping inertial neural networks with fading channels is obtained with the help of the criteria. Finally, an illustrative example is given to verify the effectiveness and validity of the obtained method.

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“…In [21], a new SOSMC was proposed for the nonlinear system by using the backstepping-like method and virtual control strategy. Based on the Lyapunov method, an adaptive SOSMC was designed in [22] for nonlinear systems with uncertainties. However, the authors in the researches [18]- [22] assumed that the state variables of the system are available.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the Lyapunov method, an adaptive SOSMC was designed in [22] for nonlinear systems with uncertainties. However, the authors in the researches [18]- [22] assumed that the state variables of the system are available. This is not feasible in practice due to expensive sensor costs or some unmeasurable variables.…”

Section: Introductionmentioning

confidence: 99%

Single Phase Second Order Sliding Mode Controller for Mismatched Uncertain Systems with Extended Disturbances and Unknown Time-Varying Delays

Nguyen

Hien

Phan

2022

J. ADV. ENG. COMPUT.

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In this paper, a novel single phase second order sliding mode controller (SPSOSMC) is proposed for the mismatched uncertain systems with extended disturbances and unknown time-varying delays. The main achievements of this study consist of three tasks: 1) a reaching phase in conventional sliding mode control (CSMC) technique is removed to ensure the global stability of the system; 2) an inﬂuence of the undesired high-frequency oscillation phenomenon in control input is vanished; 3) an exogenous perturbation is generally extended to the k-order disturbance of state variable. Firstly, a single phase switching manifold function is defined to eliminate the reaching phase in CSMC. Secondly, an unmeasurable state variable is estimated by using the proposed reduced-order sliding mode observer (ROSMO) tool. Next, a SPSOSMC is built based on the help of ROSMO tool and output information only. Then, a sufficient condition is established by employing the linear matrix inequality (LMI) technique and Lyapunov function theory such that the resulting sliding mode dynamics is asymptotically stable. Finally, a numerical example is simulated via the well-known MATLAB software to validate the eﬀectiveness of the proposed technique.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Adaptive Second-Order Sliding Mode Control: A Lyapunov Approach

Cited by 69 publications

References 27 publications

A Generalized Supertwisting Algorithm

A Generalized Supertwisting Algorithm

Passive state estimation for Markov jumping inertial neural networks under fading channels

Single Phase Second Order Sliding Mode Controller for Mismatched Uncertain Systems with Extended Disturbances and Unknown Time-Varying Delays

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