Iterative learning control and repetitive control

Freeman, Chris T.; Tan, Ying

doi:10.1080/00207179.2011.596574

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…Theorem 2. Regardless of system (1), the uncertainty, i.e., Δ A, Δ Δ C, and B are assumed to 0, if there is A positive definite matrix Q > 0, matrix N1, and N2 and scalar tau >0, epsilon 1 > 0 make the matrix inequality Ω < 0, the nonzero symmetric matrix elements: 14) is stable under the action of the actuator fault satisfies the admissible condition (7) and the iterative learning fault-tolerant control law (11), where ρ � ζ − 2 , modify the gain matrix of update quantity (13…”

Section: Stability Analysismentioning

confidence: 99%

“…e discrete repetition process ( 14) was stable under the condition of failure tolerance (7) and iterative learning robust fault-tolerant control law (11), and the modified update quantity ( 13) was the gain matrix…”

Section: Theorem 3 Considers the Case Where System (1) Has Uncertainty Where It Is Falsementioning

confidence: 99%

“…Iterative learning control uses the tracking error and original data of the system to continuously modify the current input target, so that the system output can quickly track the expected output within a limited time [6][7][8][9]. Based on iterative learning control system that can be regarded as a kind of repeated processes based on batch axis and the timeline, the system control problem of the research in recent years came to the attention of academia at home and abroad and was successfully used in multiaxis truss type robot, injection molding machine, and electric motorcycle motor system in the practical repeat operations such as industrial object [3,[10][11][12]. As the process of industrialization continues to accelerate, industrial systems are becoming more and more complex, and people's requirements for system reliability and safety are getting higher and higher, making fault diagnosis and fault-tolerant control in the past few decades [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Novel LMI‐Based Iterative Learning Robust Nonlinear Controller

et al. 2021

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An iterative learning robust fault-tolerant control algorithm is proposed for a class of uncertain discrete systems with repeated action with nonlinear and actuator faults. First, by defining an actuator fault coefficient matrix, we convert the iterative learning control system into an equivalent unknown nonlinear repetitive process model. Then, based on the mixed Lyapunov function approach, we describe the stability of the nonlinear repetitive mechanism on time and trial indices and have appropriate conditions for the repeated control system’s stability in terms of linear matrix inequality theory. Through LMI techniques, we have obtained satisfactory results and controller stability, and robustness against fault tolerance is also discussed in detail. Finally, the simulation results of the output tracking control of the two exemplary models verify the effectiveness of the proposed algorithm.

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Section: Stability Analysismentioning

confidence: 99%

Section: Theorem 3 Considers the Case Where System (1) Has Uncertainty Where It Is Falsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Novel LMI‐Based Iterative Learning Robust Nonlinear Controller

et al. 2021

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“…Classical iterative learning control (Bu et al, 2015; Yan and Ruan, 2017) and repetitive control (Freeman and Tan, 2011; Wang et al, 2009) operate based on the same concept: an internal model (Marino and Tomei, 2013) is embedded in the feed-through loop, and the input and output data from the current iteration are used to improve the performance of the next iteration. In this section, a learning controller is employed for a periodic reference signal system over an infinite time interval.…”

Section: Voltage Learning Control Using a Noncausal Low-pass Filtermentioning

confidence: 99%

Data-driven multi-inverter cooperative control for voltage tracking and current sharing in islanded AC microgrids

Hou

2019

Transactions of the Institute of Measurement and Control

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A data-driven cooperative control scheme utilizing additional local and network sensor information is proposed for voltage tracking and current sharing under a cyber-physical system framework. An information-based inverter controller is developed as a replacement for the physical mechanism or model-based method because of uncertainties related to the inverter application scenario, system structure and specifications. To overcome the disadvantages of the reference voltage set-point strategy, an integrated cascade controller is adopted in which the outer loop is a learning controller with a noncausal filter, used to achieve high-precision voltage tracking, and the inner loop is a current consensus regulator implemented by means of nonlinear proportional–integral–derivative (PID) control, used to realize instantaneous value smoothing and output current consensus among a number of inverters. The designed controller does not rely on the plant model, structure or parameters and does not use the droop mechanism; it relies on fully data-driven and distributed (without root nodes or leaders) techniques. This controller design scheme can be uniformly applied for different kinds of distributed generation unit to achieve flexible and expandable microgrid deployment. Plug-and-play and load change capabilities, as well as tolerance to communication constraints, are also verified on the MATLAB/Simulink platform.

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“…After that, a large amount of papers have been published on various issues of ILC. To name a few, special issues were launched by International Journal of Control [14,15], Asian Journal of Control [16,17], and Journal of Process Control [18]. For survey papers the readers can refer to [19][20][21][22][23], where different emphases are highlighted.…”

Section: Introductionmentioning

confidence: 99%

A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control

Shen

2018

Un. Sys.

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This paper contributes to a technical overview of recent progresses on stochastic iterative learning control (ILC), where stochastic ILC implies the learning control for systems with various random signals and factors such as stochastic noises, random data dropouts and inherent random asynchronism. The fundamental principles of ILC are first briefed with emphasis on the system formulations and typical analysis methods. Then the recent progresses on stochastic ILC are reviewed in three parts: additive randomness case, multiplicative randomness case, and coupled randomness case, respectively. Three major approaches, i.e., expectation-based method, Kalman filtering-based method, and stochastic approximation-based method, are clarified. Promising research directions are also presented for further investigation.

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Iterative learning control and repetitive control

Cited by 14 publications

References 5 publications

Design and Implementation of Novel LMI‐Based Iterative Learning Robust Nonlinear Controller

Design and Implementation of Novel LMI‐Based Iterative Learning Robust Nonlinear Controller

Data-driven multi-inverter cooperative control for voltage tracking and current sharing in islanded AC microgrids

A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control

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