A new sliding mode-based learning control for uncertain discrete-time systems

Tuan, Do Manh; Man, Zhihong; Zhang, Cishen; Jin, Jiong

doi:10.1109/icarcv.2012.6485250

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…In order to proceed with the controller design we have adopted. In this work, a robust sliding based learning controller which is given by [25], [27]:…”

Section: Smlc Formulation For the Lm Systemmentioning

confidence: 99%

“…It has been proved that if τ is equal to the sampling time. Then, the flowing inequality can be hold [25], [27]:…”

Section: Smlc Formulation For the Lm Systemmentioning

confidence: 99%

“…where β > 0 is a large constant number chosen to satisfy specific conditions as will be illustrated late. It should be noted that the inequality ( 16) is known as Lipschitz-like [25], [27]. Based on ( 16), V (t) in ( 15) can be written in the following format:…”

Section: System Stability and Convergence Analysismentioning

confidence: 99%

“…Motivated by the recent success of the LM system and the robust SMC as well as their valuable application. The basic idea of this work is to explore a new way for the development of SMC based on [25]- [27] which will lead to improve the performance of the LM in terms of handling the occurrence of external uncertainties and eliminating the chattering phenomena. Inspired by the concept of the "Lipschitz-like condition" which proposed in [25], [26], our sliding mode learning control (SMLC) approach is established.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the concept of the "Lipschitz-like condition" which proposed in [25], [26], our sliding mode learning control (SMLC) approach is established. From the previous approaches presented in [25]- [27], adopting Lipschitz-like condition enforces our controller to be independent on prior knowledge about system uncertainties. This is due to the uncertainties of the dynamics being implicitly embedded in the Lipschitz-like condition.…”

Section: Introductionmentioning

confidence: 99%

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A tracking control design for linear motor using robust sliding mode learning control

Al-nasrawi

Al-Ghanimi

Mashkor

2022

IJPEDS

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<div id="i4c-draggable-container" style="position: fixed; z-index: 1499; width: 0px; height: 0px;"><div class="resolved" style="all: initial;" data-reactroot=""> </div></div><p>In this paper, a tracking robust sliding mode learning control (SMLC) is proposed for a linear motor (LM) system. The proposed controller approach, SMLC, can guarantee a zero tracking error in the absence and presence of system uncertainties of the LM system. Unlike other classical sliding mode control (CSMC), the proposed control system is designed without any prior knowledge of the system perturbation which would facilitate control design and simplify its practical applications. To this end, a recursive learning technique is used which simultaneously adapted based on the previous information of the closed loop system stability. The system stability and convergence analysis are rigorously proved in the sense of the Lybenouve criteria. Finally, simulations results are presented to demonstrate the validity and effectiveness of the SMLC over the CSMC in terms of tracking performance and chattering alleviation.</p>

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“…In order to proceed with the controller design we have adopted. In this work, a robust sliding based learning controller which is given by [25], [27]:…”

Section: Smlc Formulation For the Lm Systemmentioning

confidence: 99%

“…It has been proved that if τ is equal to the sampling time. Then, the flowing inequality can be hold [25], [27]:…”

Section: Smlc Formulation For the Lm Systemmentioning

confidence: 99%

Section: System Stability and Convergence Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A tracking control design for linear motor using robust sliding mode learning control

Al-nasrawi

Al-Ghanimi

Mashkor

2022

IJPEDS

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Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

Wu,

Zhao,

et al. 2024

IEEE/CAA J. Autom. Sinica

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This paper presents an asynchronous outputfeedback control strategy of semi-Markovian systems via sliding mode-based learning technique. Compare with most literature results that require exact prior knowledge of system state and mode information, an asynchronous output-feedback sliding surface is adopted in the case of incompletely available state and non-synchronization phenomenon. The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem. Based upon the co-occurrence of two subsystems, the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions. Furthermore, a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion. Finally, the proposed theoretical method are substantiated through two numerical simulations with the practical continuous stirred tank reactor and F-404 aircraft engine model.

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A tracking control design for a DC motor using robust sliding mode learning control

Hadi,

Alamili,

Abbas

2023

IJPEDS

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<p>The proposed robust sliding mode learning control (RSMLC) is a new control<br />approach that uses immediate feedback from the closed-loop system to improve tracking performance. A recursive learning technique is integrated with the sliding mode controller to ensure that the tracking error and sliding variables asymptotically converge to zero, which can be guaranteed within the framework of the proposed control approach. Moreover, the proposed controller design does not require system uncertainty and its upper limits. Thus, these benefits can be significantly simplified and mitigated by the design and implementation of RSMLC for DC motor applications. In comparison with conventional sliding mode control (CSMC), the RSMLC structure does not contain an explicit switching element, so the chattering phenomenon will be eliminated. Meanwhile, it will preserve the CSMC’s durability feature. Based on Lyapunov criteria, the stability and convergence analysis of the proposed controller were rigorously proved. Additionally, CSMC and SMLC controllers have been shown to outperform proportional integral derivative (PID) controllers in systems with nonlinear dynamics, high-order systems, or uncertainties. Finally, simulation studies of the DC<br />motor system were carried out under the proposed controller. In contrast, the CSMC simulation results are also presented for comparison purposes and to verify the validity and effectiveness of the proposed RSMLC via CSMC and PID controllers.</p>

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A new sliding mode-based learning control for uncertain discrete-time systems

Cited by 3 publications

References 21 publications

A tracking control design for linear motor using robust sliding mode learning control

A tracking control design for linear motor using robust sliding mode learning control

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

A tracking control design for a DC motor using robust sliding mode learning control

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