Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks

Zhihe Zhuang

doi:10.1016/j.automatica.2025.112781

Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks

2026-01-21

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DOI：10.1016/j.automatica.2025.112781

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Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances. This paper develops a novel direct data-based ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level. Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems' Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Keywords:

Iterative learning control

Time-scale-varying task

Data-based control

Reinforcement learning

Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks

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