Robust iterative learning control synthesized with sliding-mode control for output tracking

Wen, Chenglin; Yeh, Chih-Ping; Chen, YangQuan

doi:10.1109/cdc.2011.6160762

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…LVSC controllers have been used in a variety of applications, including torque-ripple reduction in permanent magnet synchronous motor, 7 control of Czochralski process in semiconductor industry, 8 electro-pneumatic, 9 quadrotor UAVs, 10 and so on. [11][12][13][14] provide research publications on the ILC-SMC combo. However, control input discontinuities causes chattering and produces inconsistent control action.…”

Section: Introductionmentioning

confidence: 99%

“…Continuous SMC was created in Reference 6 to alleviate the chattering difficulty by providing an integration approach, although it was not a learning control. LVSC controllers have been used in a variety of applications, including torque‐ripple reduction in permanent magnet synchronous motor, 7 control of Czochralski process in semiconductor industry, 8 electro‐pneumatic, 9 quadrotor UAVs, 10 and so on 11‐14 . provide research publications on the ILC‐SMC combo.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of iterative learning control with different sliding mode algorithms for the slip‐ratio control of an electric vehicle

Denny

Kumar

2023

Intl J Robust & Nonlinear

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Developing a control strategy that can handle model uncertainties, external disturbances, and parameter fluctuations is essential for the desired operation of a nonlinear system. An attempt is made to develop such a control algorithm using the Learning Variable Structure Control technique. The proposed controllers have the advantages of both iterative learning control (ILC) and sliding mode control (SMC) such that they can compensate both structured and unstructured uncertainties. They are made comprised of modified-twisting algorithm and super-twisting algorithm respectively with the ILC technique.In this article, the performance of the two developed controllers is compared to a traditional SMC-based ILC approach. Based on simulation results, the super-twisting algorithm based ILC is found to be superior with reference to speed of convergence and tracking accuracy. This method is used to control the slip-ratio of an electric vehicle as a specific application in this paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance evaluation of iterative learning control with different sliding mode algorithms for the slip‐ratio control of an electric vehicle

Denny

Kumar

2023

Intl J Robust & Nonlinear

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“…In Tian and Yu [16], a robust ILC method combining with backstepping technique was proposed for a class of nonlinear systems with structured periodic and unstructured aperiodic uncertainties, and asymptotic stability was guaranteed by using a learning evaluation function. A robust ILC synthesized with SMC was proposed in Chen et al [17] for improving the tracking performance and accuracy. In Liu et al [18], a robust ILC scheme via adaptive SMC was proposed for reducing the torque ripple of the permanent magnet synchronous motor (PMSM), in which ILC is employed to reduce the periodic torque ripples and the SMC is used to compensate the parameter variations and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Robust iterative learning control via adaptive backstepping for oscillation displacement system of continuous casting mold driven by a servo motor

Gao

et al. 2023

Asian Journal of Control

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In order to solve the tracking problem of oscillation displacement system of continuous casting mold driven by a servo motor in the present of the time‐varying load, parameter uncertainties, unknown dynamics and external disturbances, a robust iterative learning control (ILC) via adaptive backstepping is proposed in this paper. Firstly, backstepping technique is used to design controller due to the mismatched disturbances. Secondly, the periodic component of the lumped disturbances is estimated by ILC method, while the aperiodic component is attenuated by switching term with adaptive gain. In addition, a time‐varying boundary layer approach is employed to overcome the identical initial condition limitation of the ILC. Through a proper composite energy function, the convergence of the closed‐loop system is analyzed. Finally, simulation and experimental results are given to illustrate the effectiveness of the proposed method.

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“…Armstrong and Alleyne (2020) proposed a fast cross-coupled ILC scheme using a novel error term in the update law based on the SMC technique, which improved the converging speed and the accuracy. Chen et al (2011Chen et al ( , 2012 proposed an ILC design that combined SMC on the derivative of the control signal, which eliminated the chattering, and avoided the chattering in the controller. Lei et al (2015) applied an iterative learning compensation as a supplement to the SMC controller for the low-speed servo system.…”

Section: Introductionmentioning

confidence: 99%

Robust iterative learning control design based on iterative integral sliding mode for nonlinear systems with unrepeatable disturbance

Zhang

2022

Transactions of the Institute of Measurement and Control

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For the repetitive tracking control tasks, the unrepeatable disturbance is an obstruction to learning control schemes. This paper discussed the robust iterative learning control (RILC) design based on the sliding mode control (SMC) technique for a class of nonlinear systems with unrepeatable disturbances. In order to make the tracking error converge to zero in the iteration domain under the sliding mode and eliminate the reaching phase of the SMC, an iterative integral sliding mode (IISM) surface design was proposed. The IISM leads to linear tracking error dynamics in the iteration domain, which guarantees the convergence if the sliding mode parameters meet the given condition. After that, the iterative learning control (ILC) based on the IISM was designed, which drove the sliding mode variable of the closed-loop system to reach the origin with iterations from the initial time instant, despite the effect of the unrepeatable disturbances. The robustness to the unrepeatable disturbance and the fast convergence were achieved. Finally, the proposed IISM method was applied to a one-link robotic manipulator, and the simulation results validated the performance of the proposed control design.

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Robust iterative learning control synthesized with sliding-mode control for output tracking

Cited by 3 publications

References 14 publications

Performance evaluation of iterative learning control with different sliding mode algorithms for the slip‐ratio control of an electric vehicle

Performance evaluation of iterative learning control with different sliding mode algorithms for the slip‐ratio control of an electric vehicle

Robust iterative learning control via adaptive backstepping for oscillation displacement system of continuous casting mold driven by a servo motor

Robust iterative learning control design based on iterative integral sliding mode for nonlinear systems with unrepeatable disturbance

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