Robust learning and control of time-delay nonlinear systems with deep recurrent Koopman operators

In this work, we consider the problem of Koopman modeling and data-driven predictive control for a class of uncertain nonlinear systems subject to time delays. A robust deep learning-based approach-deep recurrent Koopman operator is proposed. Without requiring the knowledge of system uncertainties o...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Han, Minghao, Li, Zhaojian, Yin, Xiang, Yin, Xunyuan
مؤلفون آخرون: School of Chemistry, Chemical Engineering and Biotechnology
التنسيق: مقال
اللغة:English
منشور في: 2024
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/174946
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:In this work, we consider the problem of Koopman modeling and data-driven predictive control for a class of uncertain nonlinear systems subject to time delays. A robust deep learning-based approach-deep recurrent Koopman operator is proposed. Without requiring the knowledge of system uncertainties or information on the time delays, the proposed deep recurrent Koopman operator method is able to learn the dynamics of the nonlinear systems autonomously. A robust predictive control framework is established based on the deep Koopman operator. Conditions on the stability of the closed-loop system are presented. The proposed approach is applied to a chemical process example. The results confirm the superiority of the proposed framework as compared to baselines.