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, Kai

doi:10.1002/acs.3506

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…However, it is worth pointing out that Assumption 2 may be difficult to achieve in some cases, because it is usually hard to determine the unique desired input u d (i, j) for the 2-D reference trajectory. To avoid using the Assumption 2, an alternative analysis approach for ILC is to directly consider the tracking errors e k (i + 1, j + 1) and e k+1 (i + 1, j + 1) in [9] and [24]. Assumption 3: Let the reference trajectory y d,k (i, j), system parameters A 1,k (i + 1, j), A 2,k (i, j), A 3,k (i, j + 1), B k (i, j) and C k (i, j), boundary states x k (i, 0) and x k (0, j) be the progressively convergent, i.e.,…”

Section: Problem Statementmentioning

confidence: 99%

Robust Iterative Learning Control for 2-D Linear Nonrepetitive Discrete Systems With Iteration-Dependent Trajectory

Wan

Xie

2022

IEEE Access

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In the existing robust iterative learning control (ILC) for 2-D discrete systems, they typicallly require to satisfy a core hypothesis that the strict repetitiveness of tracking reference trajectory and system model should be satisfied. This paper first investigates the robustness and convergence of a P-type ILC law and a high-order ILC law for 2-D linear nonrepetitive discrete systems (LNDS) with arbitrarily bounded reference trajectory and iteration-dependent reference trajectory described by a high order internal model (HOIM) operator in iteration domain, respectively. It is theoretically proved by using the 2-D linear nonrepetitive inequalities that the ILC tracking error and the control input robustly converge to a bounded range, the bound of which depends continuously on the bounds of all the nonrepetitive uncertainties. If these uncertainties are progressively convergent along the iteration domain, a precise tracking on the 2-D reference trajectory can be achieved. Two illustrative examples are provided to demonstrate the validity of the presented ILC law. Additionally, some comparative result on the practical dynamical processes is given.INDEX TERMS 2-D linear discrete nonrepetitive systems (LNDS), 2-D linear nonrepetitive inequalities, iteration-dependent trajectory, robust iterative learning control.

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Section: Problem Statementmentioning

confidence: 99%

Robust Iterative Learning Control for 2-D Linear Nonrepetitive Discrete Systems With Iteration-Dependent Trajectory

Wan

Xie

2022

IEEE Access

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A vector decomposition approach to F–M model realization and its application to robust observed-state feedback control

Zhao,

Li,

Yan

et al. 2024

Journal of the Franklin Institute

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An Adaptive Learning Control for MIMO Nonlinear System with Nonuniform Trial Lengths and Invertible Control Gain Matrix

Ding,

Jia,

Wei

et al. 2024

Electronics

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In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time applications owing to unknown uncertainties and unpredictable factors. In essence, replicating a control system at a consistent time interval proves challenging in practical scenarios. This paper proposes an adaptive iterative learning control (AILC) method for the multi-input–multi-output (MIMO) nonlinear system with nonuniform trial lengths and an invertible control gain matrix. Compared to the existing AILC research that features nonuniform trial lengths, the control gain matrix of the system in this paper is assumed to be invertible. Hence, the general requirement in the conventional AILC method that the control gain matrix of the system is positive-definite (or negative-definite) or even known is relaxed. Moreover, the tracking reference allows it to be iteration-varying. Finally, to prove the convergence of the system, the composite energy function is introduced and to verify the validity of the AILC method, a robot movement imitation with an uncalibrated camera system is used. The simulation results show that the actual output can track the desired reference trajectory well, and the tracking error converges to zero after 30 iterations.

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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

Cited by 3 publications

References 35 publications

Robust Iterative Learning Control for 2-D Linear Nonrepetitive Discrete Systems With Iteration-Dependent Trajectory

Robust Iterative Learning Control for 2-D Linear Nonrepetitive Discrete Systems With Iteration-Dependent Trajectory

A vector decomposition approach to F–M model realization and its application to robust observed-state feedback control

An Adaptive Learning Control for MIMO Nonlinear System with Nonuniform Trial Lengths and Invertible Control Gain Matrix

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