High-Order Internal Model-Based Iterative Learning Control for 2-D Linear FMMI Systems With Iteration-Varying Trajectory Tracking

Wan, Kai; Li, Xiaodong

doi:10.1109/tsmc.2019.2897459

Cited by 25 publications

(39 citation statements)

References 36 publications

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“…These HOIM-based ILC schemes mentioned above are all for one-dimensional (1-D) systems. Wan et al proposed an ILC scheme based on HOIM for two-dimensional (2-D) discrete-time linear systems in [51]. The iteration-varying reference trajectories are depicted by HOIM, and by virtue of the property of the HOIM-based 2-D linear inequalities and the stability theory of a 2-D Roesser model, the convergence and robustness of the HOIM-based ILC scheme are analyzed theoretically under random boundary conditions and HOIM-based boundary conditions respectively.…”

Section: B Hoim-based Ilc For Discrete-time Systemsmentioning

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: B Hoim-based Ilc For Discrete-time Systemsmentioning

confidence: 99%

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

Chai

2019

IEEE Access

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“…In spite of the promising results in the ILC study of 2-D dynamical systems, it is noticed that all the ILC designs reported in [5]- [12] are basically based on the known control direction. Generally, the applied control direction is assumed to be positive in advance and the convergence property is obtained in view of the fact that the control direction is positive.…”

Section: Introductionmentioning

confidence: 99%

“…As to the systems that are derived by two or more independent variables, the existing adaptive ILC approaches are becoming ineffective [2]- [4]. Two-dimensional (2-D) dynamical systems, that operate repetitively in finite and given domains, have attracted considerable attention as shown in [5]- [12]. In contrast to 1-D dynamical system, the control objective is no longer tracking curve, but surface in 2-D system.…”

Section: Introductionmentioning

confidence: 99%

“…To date, some authors have devoted themselves to the study of ILC in 2-D dynamical systems. And they have obtained several ILC works for 2-D dynamical systems [5]- [12]. A fuzzy ILC method was first proposed in [5] and corresponding progresses were considered in [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…And the objective of perfect tracking was obtained in [10]. With respect to high-order internal model (HOIM) strategy-based iteration-variant reference trajectory, the ILC research in [12] can also achieve perfect tracking when the iterative boundary conditions execute the same HOIM strategy as the reference, while compel the tracking error to a small neighbourhood of reference trajectory under random boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

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Design of Adaptive ILC for 2-D FMM Systems With Unknown Control Directions

Wei

Wan

2019

IEEE Access

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Among the existing adaptive iterative learning control (ILC) work concerning unknown control direction problem, no result is available for the two-dimensional (2-D) dynamical systems. In this paper, an adaptive ILC is developed for a class of 2-D dynamical systems described by the Fornasini-Marchesini model (FMM). Notably, the 2-D FMM system under investigation possesses not only random uncertainties in boundary condition and reference trajectory, but also unknown control direction. Even so, the constructed adaptive ILC combining with a modification mechanism can still guarantee the precise tracking of iteration-variant reference trajectory and the boundedness of all the system signals, as iteration number k tends to infinity. Theoretical analysis and simulation study are given to demonstrate the effectiveness of the developed adaptive ILC. INDEX TERMS Two-dimensional (2-D) dynamical systems, unknown control direction, adaptive iterative learning control (ILC), the Fornasini-Marchesini model (FMM).

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Robust decentralized iterative learning control for large‐scale interconnected systems described by 2‐D Fornasini–Marchesini systems with iteration‐dependent uncertainties including boundary states, disturbances, and reference trajectory

Wan

2022

Adaptive Control & Signal

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This article first investigates robust iterative learning control (ILC) problem of a class of large-scale interconnected systems, which consist of many subsystems described by two-dimensional linear discrete first Fornasini-Marchesini systems with iteration-dependent uncertainties arising from not only boundary states, disturbances, but also reference trajectory. A decentralized P-type ILC law without any information exchanges with other subsystems is proposed such that the ultimate ILC tracking error of each subsystem can converge to a bounded range, the bound of which depends continuously on the bounds of all iteration-dependent uncertainties considered. Especially, if these iteration-dependent uncertainties are convergent progressively along the iteration direction, perfect ILC tracking on 2-D reference trajectory can be obtained.Additionally, a modified ILC law with compensation technique is used to a class of large-scale interconnected systems composed of many subsystems represented by two-dimensional linear discrete second Fornasini-Marchesini systems. Two simulation examples are used to demonstrate the effectiveness and validity of the obtained ILC results. Finally, some comparative discussions are given.

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High-Order Internal Model-Based Iterative Learning Control for 2-D Linear FMMI Systems With Iteration-Varying Trajectory Tracking

Cited by 25 publications

References 36 publications

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

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

Design of Adaptive ILC for 2-D FMM Systems With Unknown Control Directions

Robust decentralized iterative learning control for large‐scale interconnected systems described by 2‐D Fornasini–Marchesini systems with iteration‐dependent uncertainties including boundary states, disturbances, and reference trajectory

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