Adaptive Iterative Learning Control for MIMO Systems with Nonuniform Trial Lengths and Alignment Constraints.

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Bibliographic Details
Title:	Adaptive Iterative Learning Control for MIMO Systems with Nonuniform Trial Lengths and Alignment Constraints.
Authors:	Zhu, Xuefeng¹ zxf627@sina.com, Ji, Shuai² 278885189@qq.com, Zhang, Yubo³ zhangyubo@sie.edu.cn, Wang, Hongjiang⁴ wanghongjiang@sie.edu.cn, Dai, Qin¹ daiq@sie.edu.cn
Source:	IAENG International Journal of Applied Mathematics. Mar2026, Vol. 56 Issue 3, p1041-1050. 10p.
Subjects:	Iterative learning control, Adaptive control systems, Nonlinear systems, Robotic trajectory control, Mathematical analysis, Multivariable control systems
Abstract:	This paper presents an adaptive iterative learning control (ILC) framework for multi-input multi-output (MIMO) nonlinear systems subject to nonuniform trial lengths and alignment constraints. To relax the requirement of identical initial conditions across iterations, a smooth reference-trajectory modification mechanism is introduced, while error boundedness and constraint satisfaction during the learning process are guaranteed by a bounded constraint function constructed within the barrier composite energy function (BCEF) framework. By integrating virtual tracking errors with a nonlinear saturationbased adaptive learning law, the proposed control scheme achieves robust parameter adaptation and uniform convergence of the closed-loop error dynamics in the presence of timevarying trial durations and external disturbances. A rigorous convergence analysis, developed using Lyapunov incremental functions and integral inequality theory, establishes the asymptotic convergence of the tracking errors. Simulation studies conducted on a two-degree-of-freedom robotic manipulator demonstrate that the proposed method yields rapid convergence and high-precision trajectory tracking, thereby confirming its robustness and practical applicability to complex nonlinear industrial systems. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper presents an adaptive iterative learning control (ILC) framework for multi-input multi-output (MIMO) nonlinear systems subject to nonuniform trial lengths and alignment constraints. To relax the requirement of identical initial conditions across iterations, a smooth reference-trajectory modification mechanism is introduced, while error boundedness and constraint satisfaction during the learning process are guaranteed by a bounded constraint function constructed within the barrier composite energy function (BCEF) framework. By integrating virtual tracking errors with a nonlinear saturationbased adaptive learning law, the proposed control scheme achieves robust parameter adaptation and uniform convergence of the closed-loop error dynamics in the presence of timevarying trial durations and external disturbances. A rigorous convergence analysis, developed using Lyapunov incremental functions and integral inequality theory, establishes the asymptotic convergence of the tracking errors. Simulation studies conducted on a two-degree-of-freedom robotic manipulator demonstrate that the proposed method yields rapid convergence and high-precision trajectory tracking, thereby confirming its robustness and practical applicability to complex nonlinear industrial systems. [ABSTRACT FROM AUTHOR]
ISSN:	19929978