Learning-Based Resilient FCS-MPC for Power Converters Under Actuator FDI Attacks
In this literature, we concentrate on investigating a learning-based resilient predictive control framework using variable-step event-triggered mechanism, which aims to avoid unnecessary events and enhance the system robustness subject to actuator false data injection (FDI) attacks. To be more preci...
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| Published in | IEEE transactions on power electronics Vol. 39; no. 10; pp. 12716 - 12728 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.10.2024
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| Abstract | In this literature, we concentrate on investigating a learning-based resilient predictive control framework using variable-step event-triggered mechanism, which aims to avoid unnecessary events and enhance the system robustness subject to actuator false data injection (FDI) attacks. To be more precise, to improve the robust performance of the controlled system under both actuator attacks and parametric uncertainties, a learning-based robust model predictive control (MPC) architecture is developed. In this control architecture, an online learning strategy is incorporated into a neural network weight update policy, which can provide a reinforced structure and accelerate the learning process. Meanwhile, in order to circumvent the unnecessary triggering and commutation behavior, a tentative verification of a triggering condition and a delayed triggering with a variable-step waiting horizon are embedded into the suggested event-triggered mechanism. The main feature of our development is that it not only enhances the control property under the actuator FDI attacks, but also attenuates the inherent issues of unnecessary switching losses and parametric uncertainties affecting the system, opening a wide research field for resilient finite control-set MPC. Finally, we highlight its advantages with a case study. |
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| AbstractList | In this literature, we concentrate on investigating a learning-based resilient predictive control framework using variable-step event-triggered mechanism, which aims to avoid unnecessary events and enhance the system robustness subject to actuator false data injection (FDI) attacks. To be more precise, to improve the robust performance of the controlled system under both actuator attacks and parametric uncertainties, a learning-based robust model predictive control (MPC) architecture is developed. In this control architecture, an online learning strategy is incorporated into a neural network weight update policy, which can provide a reinforced structure and accelerate the learning process. Meanwhile, in order to circumvent the unnecessary triggering and commutation behavior, a tentative verification of a triggering condition and a delayed triggering with a variable-step waiting horizon are embedded into the suggested event-triggered mechanism. The main feature of our development is that it not only enhances the control property under the actuator FDI attacks, but also attenuates the inherent issues of unnecessary switching losses and parametric uncertainties affecting the system, opening a wide research field for resilient finite control-set MPC. Finally, we highlight its advantages with a case study. |
| Author | Qiu, Lin Wang, Kui Liu, Xing Fang, Youtong Rodriguez, Jose Li, Yongdong |
| Author_xml | – sequence: 1 givenname: Xing orcidid: 0000-0001-9685-2862 surname: Liu fullname: Liu, Xing email: xingldl@zju.edu.cn organization: College of Electrical Engineering, Shanghai Dianji University, Shanghai, China – sequence: 2 givenname: Lin orcidid: 0000-0003-1236-2191 surname: Qiu fullname: Qiu, Lin email: qiu_lin@zju.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Jose orcidid: 0000-0002-1410-4121 surname: Rodriguez fullname: Rodriguez, Jose email: jose.rodriguezp@uss.cl organization: Faculty of Engineering, Universidad San Sebastian Santiago, Santiago, Chile – sequence: 4 givenname: Kui orcidid: 0000-0003-3928-9318 surname: Wang fullname: Wang, Kui email: wangkui@tsinghua.edu.cn organization: State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing, China – sequence: 5 givenname: Yongdong surname: Li fullname: Li, Yongdong email: liyd@tsinghua.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 6 givenname: Youtong orcidid: 0000-0002-8521-4184 surname: Fang fullname: Fang, Youtong email: youtong@zju.edu.cn organization: State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing, China |
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| SubjectTerms | Actuators Adaptive systems Capacitors Control systems Event-triggered mechanism false data injection (FDI) attacks finite control-set model predictive control (MPC) low switching frequency (SF) neural network (NN) Power system dynamics Predictive control resilient predictive control Uncertainty |
| Title | Learning-Based Resilient FCS-MPC for Power Converters Under Actuator FDI Attacks |
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