Adaptive Predictive Iterative Learning Control for Constrained Nonlinear Systems Under Varying Operating Environments

Qiongxia Yu

doi:10.1109/TASE.2026.3654589

Adaptive Predictive Iterative Learning Control for Constrained Nonlinear Systems Under Varying Operating Environments

2026-01-15

0

PRE

AI

DOI：10.1109/TASE.2026.3654589

Original

Original request for help

Save

Abstract

En 中文

In this work, a new adaptive predictive iterative learning control (APILC) scheme is designed for a class of multiple-input-multiple-output (MIMO) discrete-time nonlinear systems, which simultaneously addresses the problems of randomly varying iteration lengths, iteration-time-varying system uncertainties on parameters and disturbances, iteration-time-varying reference trajectories, and system constraints. First, in order to compensate for missing output/state data caused by randomly varying iteration lengths, a new search decision compensation mechanism (SDCM) is constructed to select optimal data from historical data, estimated data, and predicted data, thereby mitigating the impact of randomly varying iteration lengths on the tracking performance. Next, a new adaptive learning algorithm is developed based on the compensated data, which not only estimates and predicts iteration-time-varying system uncertainties on parameters and disturbances, but also constructs a more accurate adaptive prediction model to effectively capture future dynamic characteristics of the system. Furthermore, the constructed adaptive prediction model is employed to design an APILC scheme that simultaneously handles both iteration-time-varying reference trajectories and system constraints. Theoretically, the convergence of both the adaptive prediction model and the tracking error is rigorously guaranteed, even under system constraints and various iteration-time-varying operating environments. Ultimately, the simulation results verify the effectiveness of the proposed SDCM based APILC (SDCM-APILC) scheme. Note to Practitioners—Many industrial control systems, such as robotic arms, semiconductor manufacturing equipment, and high-speed trains, operate repetitively but face challenges like randomly varying iteration lengths, iteration-time-varying system uncertainties on parameters and disturbances, iteration-time-varying reference trajectories, and strict system constraints. Traditional iterative learning control (ILC) schemes often fail to handle these complexities simultaneously, leading to degraded performance or instability. This work develops an adaptive predictive iterative learning control (APILC) scheme that compensates for missing operational data caused by randomly varying iteration lengths through a novel search decision compensation mechanism (SDCM), which ensures effective operation of practical controlled systems even when operational data is missing. Furthermore, the proposed scheme adapts to iteration-time-varying dynamics by online estimating and predicting unknown system parameters and disturbances, as well as adjusting control inputs accordingly, while explicitly addressing iteration-time-varying reference trajectories, thereby enabling practical controlled systems to achieve stronger adaptability to unknown iteration-varying operating environments. Meanwhile, the proposed scheme can effectively address system constraints commonly encountered in practical systems, thus guaranteeing operational safety and stability. The convergence of the proposed adaptive predictive iterative learning control scheme is rigorously analyzed through theoretical derivations. In fact, the proposed control scheme can be applied to various practical applications such as robotic systems, train systems, etc.

Keywords:

Iterative learning control

adaptive iterative learning control

predictive iterative learning control

randomly varying iteration lengths

system constraints

Adaptive Predictive Iterative Learning Control for Constrained Nonlinear Systems Under Varying Operating Environments

Abstract

Journal

Organization