Convergence Analysis of Sampled-Data ILC for Locally Lipschitz Continuous Nonlinear Nonaffine Systems With Nonrepetitive Uncertainties

Chi, Ronghu; Yu, Hui; Chien, Chiang‐Ju; Huang, Biao; Hou, Zhongsheng

doi:10.1109/tac.2020.3020803

Cited by 12 publications

(19 citation statements)

References 25 publications

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“…It is worth highlighting that, nonrepetitive disturbances, i.e., Def. 4, have already been widely studied in the literature, e.g., [11], [20], [26], [28]. However, the other types of disturbances have not been properly analyzed yet.…”

Section: Problem Definitionmentioning

confidence: 99%

“…However, it is still under-studied for continuous-time systems. In [26], the sampled-data ILC algorithm for continuous-time systems can manage the time nonrepetitive disturbances, while in [27], the Authors tackle the same problem in the case of systems with a fixed relative degree equal to one, constant linear input and output fields, and saturated inputs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

Pierallini¹,

Angelini

Mengacci

et al. 2021

IEEE Access

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Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

Pierallini¹,

Angelini

Mengacci

et al. 2021

IEEE Access

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“…In recent years, some data-driven ILC (DDILC) methods have been proposed for general nonaffine nonlinear plants to avoid any use of the explicit model information. [30][31][32][33][34][35] The robust analysis of DDILC is presented in Reference 33 considering the nonrepetitive initial states, disturbances, and target trajectories together. Further, an iterative adaptive mechanism is introduced and an adaptive DDILC is proposed in Reference 34 to deal with nonrepetitive references for achieving an iteratively asymptotic convergence.…”

Section: Introductionmentioning

confidence: 99%

Data‐based analysis of iterative learning control for MIMO nonaffine nonlinear systems with multiple nonrepetitive uncertainties

Chi

Liu

2023

Intl J Robust & Nonlinear

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Two challenging problems are addressed in this work for the convergence analysis of iterative learning control (ILC), that is, the rigorous assumption on repetitive conditions and the severe dependence on linear or nonlinear parametric models. Consequently, a data‐based analysis method of ILC is presented for a general multi‐input multi‐output nonaffine nonlinear system in the existence of multiple nonrepetitive uncertainties in initial states, external disturbances, reference trajectories, and plant models. An extended iterative dynamic relationship is constructed to extract the linear relationship of I/O dynamics for a nonaffine nonlinear system between two different iterations. The data‐based double dynamics analysis method is developed for analyzing that the tracking error is convergent and the I/O sequence is bounded. The tracking error is shown to converge to a finite bound related to nonrepetitive uncertainties and achieve a perfect convergence if nonrepetitive uncertainties are also iteratively convergent to zero. Notably, the presented analysis method merely uses the I/O data without relying on the plant model. Simulations validate the effectiveness of our theoretical results.

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“…However, the ILC designs in [8][9][10][11] depend on the assumption that some factors such as reference trajectory, initial conditions and disturbances should be strictly iteration-invariant, which does not match the 'practical nature' of ILC [12]. Therefore, over the past years, many studies have been devoted to deal with the issue of iteration-varying (nonrepetitive) uncertainties in ILC designs, for example, see [12][13][14][15][16][17][18][19][20]. Despite these significant results, all of them have focused on systems without time-delay.…”

Section: Introductionmentioning

confidence: 99%

Robust iterative learning control for uncertain continuous‐time system with input delay and random iteration‐varying uncertainties

Shokri-Ghaleh

Ganjefar

Shahri

2021

IET Control Theory & Appl

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This study deals with the problem of robust iterative learning control (ILC) for linear continuous-time systems with input delay subject to uncertainties in input delay, plant dynamic, reference trajectory, initial conditions and disturbances. Using the internal model control (IMC) structure in the frequency domain, an ILC scheme is proposed in which the IMC structure is responsible for coping with uncertainties in both delay time and plant dynamic. Sufficient conditions are derived to ensure that the tracking error expectation is bounded and converges monotonically to a small neighbourhood of zero (in the L 2 -norm sense) when uncertainties in reference trajectory, initial conditions and disturbances vary randomly from trial to trial. It is shown that the derived conditions are still valid to guarantee both boundedness and monotonic convergence of the tracking error variance (in the L 2 -norm sense). Illustrative examples are provided to demonstrate the effectiveness of the proposed method.

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Convergence Analysis of Sampled-Data ILC for Locally Lipschitz Continuous Nonlinear Nonaffine Systems With Nonrepetitive Uncertainties

Cited by 12 publications

References 25 publications

A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

Data‐based analysis of iterative learning control for MIMO nonaffine nonlinear systems with multiple nonrepetitive uncertainties

Robust iterative learning control for uncertain continuous‐time system with input delay and random iteration‐varying uncertainties

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