Iterative Learning Control for Output Tracking of Nonlinear Systems With Unavailable State Information

Li, Xuefang; Shen, Dong; Ding, Beichen

doi:10.1109/tnnls.2021.3062633

Cited by 16 publications

(12 citation statements)

References 29 publications

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“…Applying Lemma 1 to (21) with convergence condition (10) and considering (17) and (20), we can derive…”

Section: Hoilc Design and Convergence Analysismentioning

confidence: 99%

“…First-order ILC, which generates the control input from tracking information at last iteration, is widely applied to dynamical systems for perfect tracking in a finite time interval [20][21][22][23][24][25][26]. However, only the tracking information of last iteration is utilized to update the current control input in first-order ILC, and thus it is difficult to obtain a satisfactory convergence speed.…”

Section: Introductionmentioning

confidence: 99%

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Higher‐Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

et al. 2021

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For the nonlinear discrete-time system, higher-order iterative learning control (HOILC) with optimal control gains based on evolutionary algorithm (EA) is developed in this paper. Since the updating actions are constituted by the tracking information from several previous iterations, the suitably designed HOILC schemes with appropriate control gains usually achieve fast convergence speed. To optimize the control gains in HOILC approach, EA is introduced. The encoding strategy, population initialization, and fitness function in EA are designed according to the HOILC characteristics. With the global optimization of EA, the optimal control gains of HOILC are selected adaptively so that the number of convergence iteration is reduced in ILC process. It is shown in simulation that the sum absolute error, total square error, and maximum absolute error of tracking in the proposed HOILC based on EA are convergent faster than those in conventional HOILC.

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“…Applying Lemma 1 to (21) with convergence condition (10) and considering (17) and (20), we can derive…”

Section: Hoilc Design and Convergence Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Higher‐Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

et al. 2021

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“…Iterative learning control (ILC) is famous for its excellent ability to handle repetitive tracking control and reject periodic disturbances for nonlinear uncertain systems operating during a finite time interval [1]- [4]. Thanks to simple computation and high accuracy, ILC has earned a lot of interest in the past decades [5]- [13]. Until now, people in this field have been facilitating ILC algorithms, such as contractionmapping ILC algorithm and adaptive ILC algorithm, into various practical applications, e.g., power electronics and chemical industry.…”

Section: Introductionmentioning

confidence: 99%

Initial-Rectification Neuro-Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors

et al. 2023

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An initial-rectification adaptive iterative learning control scheme is proposed to solve the angle tracking problem of robot manipulators with input deadzone under nonzero initial errors. Lyapunov approach is utilized to design the controller. First, the initial-rectification auxiliary reference signal is constructed to overcome the obstacle caused by nonzero initial errors during ILC design. Second, adaptive ILC strategy and robust control strategy are adopted for dealing with deadzone nonlinearity. In addition, adaptive learning neural network is applied to approximate for uncertainties. The stability of closed-loop robotic system is rigorously proven by theoretical analysis. In the end, numerical simulation results are provided to verify the effectiveness of the proposed adaptive iterative learning control scheme.

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“…I TERATIVE learning control (ILC), as an effective and unsupervised control method, has been extensively used in addressing the finite-time-based trajectory tracking problem for systems with nonrepetitive uncertainties, such as linear systems [1]- [4], stochastic systems [5]- [6], multi-agent systems [7]- [8], and nonrepetitive systems [9]- [11]. In [9]- [10], the ILC tracking problem for 1-D nonrepetitive discrete systems with nonrepetitive uncertainties in initial states, external disturbances, plant model matrices and desired reference trajectories was investigated.…”

Section: Introductionmentioning

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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Iterative Learning Control for Output Tracking of Nonlinear Systems With Unavailable State Information

Cited by 16 publications

References 29 publications

Higher‐Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

Higher‐Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

Initial-Rectification Neuro-Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors

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

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