Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2024, 7(5); doi: 10.25236/AJCIS.2024.070524.

Data-driven Tracking Control of Nonlinear Systems under Deception Attacks and Packet Loss

Author(s)

Yipeng Liu

Corresponding Author:
Yipeng Liu
Affiliation(s)

College of Information Engineering, Nanjing University of Finance & Economics, Nanjing, 210023, China

Abstract

In this paper, the tracking control problem of nonlinear systems subject to deception attacks and random packet loss is investigated. Firstly, the system with an unknown model is dynamically linearized by using the pseudo partial derivative (PPD). Secondly, based on the model-free adaptive control (MFAC) approach, a data-driven controller model is designed which is solely dependent on the input/output (I/O) data, and the influence of deception attacks and packet loss is considered. Thirdly, the effectiveness of the data-driven tracking control method is demonstrated by stability analysis, the validity is confirmed by a simulation example.

Keywords

Data-Driven, Model-Free Adaptive Control (MFAC), Pseudo Partial Derivative (PPD), Deception Attacks, Packet Loss

Cite This Paper

Yipeng Liu. Data-driven Tracking Control of Nonlinear Systems under Deception Attacks and Packet Loss. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 180-189. https://doi.org/10.25236/AJCIS.2024.070524.

References

[1] Z.-H. Pang, C. -D. Bai, S. Liu, Q. -L. Han and X. -M. Zang. A novel networked predictive control method for systems with random communication constraints [J]. Journal of Systems Science and Complexity, 2021, 34: 1364-1378.

[2] S. Liu, Z. -S. Hou, T. Tian, Z. Deng and Z. Li. A novel dual successive projection-based model-free adaptive control method and application to an autonomous Car [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 30(11):3444-3457.

[3] Y. Liao, Q. Jiang, T. Du and W. Jiang. Redefined output model-free adaptive control method and unmanned surface vehicle heading control [J]. IEEE Journal of Oceanic Engineering, 2020, 45(3):714-723.

[4] X. Liu, L. Qiu, Y. Fang and J. Rodríguez. Predictor-based data-driven model-free adaptive predictive control of power converters using machine learning [J]. IEEE Transactions on Industrial Electronics, 2023, 70(8):7591-7603.

[5] Z. -S. Hou and T. Lei. Constrained model free adaptive predictive perimeter control and route guidance for multi-region urban traffic systems [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(2): 912-924.

[6] Q. Jiang, Y. Liao, Y. Li, J. Fan and Y. Miao. Heading control of unmanned surface vehicle with variable output constraint model-free adaptive control algorithm [J]. IEEE Access, 2019, 7: 131008-131018.

[7] N. Yang, R. Gao, Y. Feng and H. Su. Event-triggered impulsive control for complex networks under stochastic deception attacks [J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 1525-1534.

[8] Y. Asadi, M. M. Farsangi, A. M. Amani, E. Bijami and H. H. Alhelou. Data-driven automatic generation control of interconnected power grids subject to deception attacks [J]. IEEE Internet of Things Journal, 2023, 10(9): 7591-7600.

[9] W. Yu, X. Bu and Z. -S. Hou. Security data-driven control for nonlinear systems subject to deception and false data injection attacks [J]. IEEE Transactions on Network Science and Engineering, 2022, 9(4): 2910-2921.

[10] J. Chen, C. Hua and X. Guan. Iterative learning model-free control for networked systems with dual-direction data dropouts and actuator faults [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(11): 5232-5240.