基于动态输出反馈的区间二型T-S模糊系统的模型预测安全控制
Model Predictive Security Control of Interval Type-2 T-S Fuzzy System Based on Dynamic Output Feedback
DOI: 10.12677/MOS.2023.123175, PDF,    国家自然科学基金支持
作者: 马江涛：上海理工大学理学院，上海；宋 燕*：上海理工大学光电信息与计算机工程学院，上海
关键词: 模糊模型预测控制；欺骗攻击；动态输出反馈；奇异值分解；安全；Fuzzy Model Predictive Control； Deception Attack； Dynamic Output Feedback； Singular Value Decomposition； Security
摘要: 本文研究了一类在测量输出端带有欺骗攻击的区间二型T-S模糊系统模糊模型预测控制(FMPC)的安全问题。针对不可测的系统状态、系统的非线性和欺骗攻击的破坏性，采用了FMPC框架下的动态输出反馈控制，同时提出了无穷时域上的最坏情况的优化问题，进行性能分析和控制综合。利用二次函数方法和奇异值分解技术，解决了变量之间耦合引起的非凸性，并导出了满足终端约束集的条件。此外，为了减轻攻击对递推可行性的破坏，引入了特殊标量，应用了不等式分析技术。在此基础上提出了一个具有可解性的辅助优化问题来求出所需的控制器，并得到了保证在基于FMPC的控制器下控制系统在H2意义上是均方安全的充分条件。最后，通过一个算例证明了所提方法的有效性。
Abstract: This paper studies the security of fuzzy model predictive control (FMPC) for a class of interval type-2 T-S fuzzy systems with deception attack at the measurement output. Aiming at the unmeasurable system state, the nonlinearity of the system and the destructiveness of the deception attack, the dynamic output feedback control under the FMPC framework is adopted, and the optimization problem of the worst case in the infinite time domain is proposed for performance analysis and control synthesis. By using the quadratic function method and singular value decomposition tech-nique, the non-convexity caused by the coupling between variables is solved, and the conditions to satisfy the terminal constraint set are derived. In addition, in order to reduce the damage of attack to the recursion feasibility, a special scalar is introduced and inequality analysis technology is ap-plied. On this basis, a solvable auxiliary optimization problem is proposed to find the required con-troller, and a sufficient condition is obtained to ensure that the control system is mean square safe in the sense of H2 under the FMPC-based controller. Finally, an example is given to demonstrate the effectiveness of the proposed method.
文章引用：马江涛, 宋燕. 基于动态输出反馈的区间二型T-S模糊系统的模型预测安全控制[J]. 建模与仿真, 2023, 12(3): 1908-1925. https://doi.org/10.12677/MOS.2023.123175

参考文献

[1] Kothare, M.V., Balakrishnan, V. and Morari, M. (1996) Robust Constrained Model Predictive Control Using Linear Matrix In-equalities. Automatica, 32, 1361-1379. [Google Scholar] [CrossRef] 
[2] Zou, Y., Lam, J., Niu, Y. and Li, D. (2015) Constrained Predictive Control Synthesis for Quantized Systems with Markovian Data Loss. Automatica, 55, 217-225. [Google Scholar] [CrossRef] 
[3] Yang, X., Lam, H.K. and Wu, L. (2019) Member-ship-Dependent Stability Conditions for Type-1 and Interval Type-2 T-S Fuzzy Systems. Fuzzy Sets and Systems, 356, 44-62. [Google Scholar] [CrossRef] 
[4] Dong, Y., Song, Y. and Wei, G. (2020) Efficient Model-Predictive Control for Networked Interval Type-2 T-S Fuzzy System with Stochastic Communication Protocol. IEEE Transactions on Fuzzy Sys-tems, 29, 286-297. [Google Scholar] [CrossRef] 
[5] Hu, J., Zhang, H., Liu, H. and Yu, X. (2021) A Survey on Sliding Mode Control for Networked Control Systems. International Journal of Systems Science, 52, 1129-1147. [Google Scholar] [CrossRef] 
[6] Ju, Y., Tian, X., Liu, H. and Ma, L. (2021) Fault Detection of Net-worked Dynamical Systems: A Survey of Trends and Techniques. International Journal of Systems Science, 52, 3390-3409. [Google Scholar] [CrossRef] 
[7] Liu, H., Qian, W., Xing, W. and Zhao, Z. (2021) Further Results on Delay-Dependent Robust H∞ Control for Uncertain Systems with Interval Time-Varying Delays. Systems Science & Control Engineering, 9, 30-40. [Google Scholar] [CrossRef] 
[8] Lu, Q., Shi, P., Liu, J. and Wu, L. (2019) Model Predictive Control under Event-Triggered Communication Scheme for Nonlinear Networked Systems. Journal of the Franklin Institute, 356, 2625-2644. [Google Scholar] [CrossRef] 
[9] Ma, L., Wang, Z., Chen, Y. and Yi, X. (2021) Probability-Guaranteed Distributed Filtering for Nonlinear Systems with Innovation Constraints over Sensor Networks. IEEE Transactions on Control of Network Systems, 8, 951-963. [Google Scholar] [CrossRef] 
[10] Wu, N., Li, D. and Xi, Y. (2021) Distributed Integrated Control of a Mixed Traffic Network with Urban and Freeway Networks. IEEE Transactions on Control Systems Technology, 30, 57-70. [Google Scholar] [CrossRef] 
[11] Yong, X., Shanbi, W., Hang, C., Yi, C. and Tianle, P. (2022) Predictive Control of Uncertain Systems over Networks with Redundant Link. Journal of the Franklin Institute, 359, 8621-8638. [Google Scholar] [CrossRef] 
[12] Zou, L., Wang, Z., Hu, J., Liu, Y. and Liu, X. (2021) Communica-tion-Protocol-Based Analysis and Synthesis of Networked Systems: Progress, Prospects and Challenges. International Journal of Systems Science, 52, 3013-3034. [Google Scholar] [CrossRef] 
[13] Zou, Y., Wang, Q., Jia, T. and Niu, Y. (2016) Multirate Event-Triggered MPC for NCSs with Transmission Delays. Circuits, Systems, and Signal Processing, 35, 4249-4270. [Google Scholar] [CrossRef] 
[14] Tao, H., Tan, H., Chen, Q., Liu, H. and Hu, J. (2022) H∞ State Estima-tion for Memristive Neural Networks with Randomly Occurring DoS Attacks. Systems Science & Control Engineering, 10, 154-165. [Google Scholar] [CrossRef] 
[15] Liu, L., Ma, L., Zhang, J. and Bo, Y. (2021) Distributed Non-Fragile Set-Membership Filtering for Nonlinear Systems under Fading Channels and Bias Injection Attacks. International Journal of Systems Science, 52, 1192-1205. [Google Scholar] [CrossRef] 
[16] Zhao, D., Wang, Z., Wei, G. and Han, Q.L. (2020) A Dynamic Event-Triggered Approach to Observer-Based PID Security Control Subject to Deception Attacks. Automatica, 120, Article ID: 109128. [Google Scholar] [CrossRef] 
[17] Chen, B., Niu, Y. and Zou, Y. (2019) Security Control for Markov Jump System with Adversarial Attacks and Unknown Transition Rates via Adaptive Sliding Mode Technique. Journal of the Franklin Institute, 356, 3333-3352. [Google Scholar] [CrossRef] 
[18] Wang, J., Song, Y. and Wei, G. (2021) Security-Based Resilient Ro-bust Model Predictive Control for Polytopic Uncertain Systems Subject to Deception Attacks and RR Protocol. IEEE Transac-tions on Systems, Man, and Cybernetics: Systems, 52, 4772-4783. [Google Scholar] [CrossRef] 
[19] Li, H., Wu, C., Yin, S. and Lam, H.K. (2015) Observer-Based Fuzzy Control for Nonlinear Networked Systems under Unmeasur-able Premise Variables. IEEE Transactions on Fuzzy Systems, 24, 1233-1245. [Google Scholar] [CrossRef] 

为你推荐