An E‐HOIM Based Data‐Driven Adaptive TILC of Nonlinear Discrete‐Time Systems for Non‐Repetitive Terminal Point Tracking

Lin, Na; Chi, Ronghu; Huang, Biao; Chien, Chiang‐Ju; Feng, Yuanjing

doi:10.1002/asjc.1635

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…This scheme greatly improves the convergence rate and has better convergence performance than simple HOIM-based ILC. Moreover, Lin et al proposed a new ATILC based on an extended concept of HOIM for nonlinear non-affine discrete-time systems in [66], to make the system state or output at the endpoint of each operation track a desired target value. An iteratively dynamical linearization method is performed for the unknown nonlinear systems, and then the inverse of the iteration-varying gradient parameter in the obtained linear data equation is approximated by the HOIM, while the known portion of the HOIM is incorporated into the learning control law.…”

Section: Applications and Extensionsmentioning

confidence: 99%

A Survey on High-Order Internal Model Based Iterative Learning Control

Chai

2019

IEEE Access

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Iterative learning control (ILC) has been developed for decades and is mainly used to solve the repetitive control tasks. However, in the actual operation of systems, there are many non-strictly repetitive or iteration-varying factors, such as the iteration-varying reference trajectory, non-repetitive system parameters, iteration-related initial states, iteration-dependent input and output disturbances, etc. In order to solve the non-strictly repetitive problems, the High-Order Internal Model (HOIM)-based ILC is proposed. HOIM can be formulated as a polynomial in the iteration domain, which is auto-regressive. HOIM-based ILC for nonlinear systems is more complex than HOIM-based ILC for linear systems, and when the system parameters change iteratively, the Lyapunov-based analysis method is used instead of traditional contraction mapping method. Not only can HOIM be integrated into the traditional ILC, it can be also combined with other control methods, such as adaptive control, terminal control, repetitive control and so on. In this paper, we review the advances in HOIM-based ILC, systematically sort out the development and main contents of HOIM, summarize its main applications and extensions, and finally put forward some further development directions. INDEX TERMS High-order internal model, iterative learning control, non-repetitiveness.

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Section: Applications and Extensionsmentioning

confidence: 99%

A Survey on High-Order Internal Model Based Iterative Learning Control

Chai

2019

IEEE Access

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“…It utilizes the control information of previous trials to improve control effect of the current operation. Normally, there are three categories of ILC methods, that is, proportional-integral-derivative (PID) type ILC (Liu et al, 2014(Liu et al, , 2018Madady, 2008Madady, , 2013Park, 1999;Ma and Li, 2010), optimal ILC (Akhrouieh and Gharaveisi, 2013;Chi and Hou, 2007;Owens, 2012;Owens et al, 2013;Yu et al, 2017b), and adaptive ILC (Chen et al, 2011;Chi et al, 2015a;Lin et al, 2018;Liu et al, 2017;Ngo et al, 2014;Yu et al, 2017a). Note that ILC is originally proposed for a general nonlinear system and requires very little system information, only the bound of the direct transmission term, and thus it is named as a ''model-free'' or ''data-driven'' approach.…”

Section: Introductionmentioning

confidence: 99%

Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

Wang

Wei

Chi

2020

Transactions of the Institute of Measurement and Control

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In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller design, an iterative dynamic linear data model is built between two consecutive iterations to formulate the linear input-output data relationship of the repetitive nonlinear nonaffine discrete-time system. The linear data model is virtual and does not have any physical meanings, which is very different to the traditional mechanism mathematical model. In the sequel, the eDDOILC is proposed by designing an objective function with respect to the proposed two-dimensional ISMS. Rigorous proof is provided to show the convergence of the proposed eDDOILC method. Furthermore, the results have been extended to a multiple-input multiple-output (MIMO) nonaffine nonlinear discrete-time repetitive system. In general, the proposed eDDOILC is data-driven where no explicit model information is included. It is illustrated that the presented eDDOILC is effective when applied to the nonlinear nonaffine uncertain systems.

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Robust high-order iterative learning control approach for two-dimensional linear discrete time-varying Fornasini–Marchesini systems with iteration-dependent reference trajectory

Wan

2021

International Journal of Systems Science

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An E‐HOIM Based Data‐Driven Adaptive TILC of Nonlinear Discrete‐Time Systems for Non‐Repetitive Terminal Point Tracking

Cited by 7 publications

References 34 publications

A Survey on High-Order Internal Model Based Iterative Learning Control

A Survey on High-Order Internal Model Based Iterative Learning Control

Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

Robust high-order iterative learning control approach for two-dimensional linear discrete time-varying Fornasini–Marchesini systems with iteration-dependent reference trajectory

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