Object

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

Abstract:

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 databased 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.

Description:

artykuł zamieszczony w: "Automatica", Vol. 185

Format:

application/pdf

Resource Identifier:

oai:zbc.uz.zgora.pl:94078

DOI:

10.1016/j.automatica.2025.112781

Pages:

1-12

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Object collections:

Last modified:

Mar 27, 2026

In our library since:

Mar 27, 2026

Number of object content hits:

1

All available object's versions:

https://zbc.uz.zgora.pl/repozytorium/publication/105890

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