Zhuang, Zhihe ; González, Rodrigo A. ; Tao, Hongfeng ; Paszke, Wojciech (1975- ) ; Oomen, Tom
Tytuł:Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks
Temat i słowa kluczowe:iterative learning control ; time-scale-varying task ; data-based control ; reinforcement learning
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.
Opis:artykuł zamieszczony w: "Automatica", Vol. 185
Data wydania: Typ zasobu: Format: DOI:10.1016/j.automatica.2025.112781
Strony: Jezyk: Prawa do dysponowania publikacją: