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access icon free Observer-based MPC for NCS with actuator saturation and DoS attacks via interval type-2 T–S fuzzy model

This study addresses an observer-based model predictive control (MPC) algorithm for a networked control system (NCS) under denial of service (DoS) attacks and actuator saturation via an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. With few studies undertaking the cyber security problems in the research of MPC algorithm, this study considers the data transmission problem when DoS attacks occur. Under DoS attacks, signals in the communication networks will be interfered. The probability of error-free packet reception depends on signal-to-interference-plus-noise ratio of the wireless transmission networks. In order to reduce the on-line computation, an off-line fuzzy observer is devised to estimate the system states. Meanwhile, an on-line MPC algorithm is proposed to minimise the performance objective function and obtain the secure model predictive controller gain. Besides, the recursive feasibility is ensured by refreshing the bound of estimation error. Finally, illustrative examples certificate the effectiveness of the presented method.

References

    1. 1)
      • 18. Farina, M., Giulioni, L., Magni, L., et al: ‘An approach to output-feedback MPC of stochastic linear discrete-time systems’, Automatica, 2015, 55, pp. 140149.
    2. 2)
      • 4. Soudbakhsh, D., Annaswamy, A., Voit, H.: ‘Adaptation in networked control systems with hierarchical scheduling’, IET Control Theory Appl., 2019, 13, (17), pp. 27752782.
    3. 3)
      • 24. Ran, M., Wang, Q., Dong, C.: ‘Stabilization of a class of nonlinear systems with actuator saturation via active disturbance rejection control’, Automatica, 2016, 63, pp. 302310.
    4. 4)
      • 10. Yu, Y., Yuan, Y.: ‘Event-triggered active disturbance rejection control for nonlinear network control systems subject to DoS and physical attacks’, ISA Trans., 2019, 85, pp. 6070.
    5. 5)
      • 36. Ding, B., Ping, X.: ‘Output feedback predictive control with one free control move for nonlinear systems represented by a Takagi-Sugeno model’, IEEE Trans. Fuzzy Syst., 2014, 22, (2), pp. 249263.
    6. 6)
      • 35. Zhang, Z., Niu, Y., Song, J.: ‘Input-to-state stabilization of interval type-2 fuzzy systems subject to cyber attacks: an observer-based adaptive sliding mode approach’, IEEE Trans. Fuzzy Syst., 2020, 28, (1), pp. 190203.
    7. 7)
      • 38. Peng, L., Shi, L., Cao, X., et al: ‘Optimal attack energy allocation against remote state estimation’, IEEE Trans. Autom. Control, 2018, 63, (7), pp. 21992205.
    8. 8)
      • 33. Zhao, T., Dian, S.: ‘State feedback control for interval type-2 fuzzy systems with time-varying delay and unreliable communication links’, IEEE Trans. Fuzzy Syst., 2018, 26, (2), pp. 951966.
    9. 9)
      • 21. Tang, X., Deng, L., Yu, J., et al: ‘Output feedback predictive control of interval type-2 T-S fuzzy systems with Markovian packet loss’, IEEE Trans. Fuzzy Syst., 2018, 26, (4), pp. 24502459.
    10. 10)
      • 11. Zhang, H., Cheng, P., Shi, L., et al: ‘Optimal DoS attack scheduling in wireless networked control system’, IEEE Trans. Control Syst. Technol., 2016, 24, (3), pp. 843852.
    11. 11)
      • 26. Huang, H., Li, D., Lin, Z., et al: ‘An improved robust model predictive control design in the presence of actuator saturation’, Automatica, 2011, 47, pp. 861864.
    12. 12)
      • 34. Lu, Q., Shi, P., Lam, H., et al: ‘Interval type-2 fuzzy model predictive control of nonlinear networked control systems’, IEEE Trans. Fuzzy Syst., 2015, 23, (6), pp. 23172328.
    13. 13)
      • 25. Yang, H., Xia, Y., Geng, Q.: ‘Analysis and synthesis of delta operator systems with actuator saturation’ (Springer, Singapore, 2019).
    14. 14)
      • 32. Imen, M., Donia, B., Chokri, R.: ‘Active fault tolerant control design for stochastic interval type-2 Takagi-Sugeno fuzzy model’, Int. J. Intell. Comput. Cybern., 2018, 11, (3), pp. 404422.
    15. 15)
      • 14. Yang, H., Guo, M., Xia, Y., et al: ‘Trajectory tracking for wheeled mobile robots via model predictive control with softening constraints’, IET Control Theory Appl., 2018, 12, (2), pp. 206214.
    16. 16)
      • 17. Ping, X., Pedrycz, W.: ‘Output feedback model predictive control of interval type-2 T-S fuzzy system with bounded disturbance’, IEEE Trans. Fuzzy Syst., 2020, 28, (1), pp. 148162.
    17. 17)
      • 8. Tian, E., Peng, C.: ‘Memory-based event-triggering H load frequency control for power systems under deception attacks’, IEEE Trans. Cybern., 2020, 50, (11) pp. 46104618.
    18. 18)
      • 6. Ding, D., Wang, Z., Ho, D., et al: ‘Observer-based event-triggering consensus control for multiagent systems with lossy sensors and cyber-attacks’, IEEE Trans. Cybern., 2017, 47, (8), pp. 19361947.
    19. 19)
      • 28. Yan, J., Yang, G., Li, X.: ‘Adaptive observer-based fault-tolerant tracking control for T-S fuzzy systems with mismatched faults’, IEEE Trans. Fuzzy Syst., 2020, 28, (1), pp. 134147.
    20. 20)
      • 20. Razavinasab, Z., Farsangi, M., Barkhordari, M.: ‘State estimation based distributed model predictive control of large-scale networked systems with communication delays’, IET Control Theory Appl., 2017, 11, (15), pp. 24972505.
    21. 21)
      • 29. López-Estrada, F., Rotondo, D., Valencia-Palomo, G.: ‘A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems’, Processes, 2019, 7, (11), pp. 814852.
    22. 22)
      • 22. Jia, S., Shan, J.: ‘Finite-time trajectory tracking control of space manipulator under actuator saturation’, IEEE Trans. Ind. Electron., 2020, 67, (3), pp. 20862096.
    23. 23)
      • 9. Yuan, Y., Yuan, H., Guo, L., et al: ‘Resilient control of networked control system under DoS attacks: a unified game approach’, IEEE Trans. Ind. Inf., 2016, 12, (5), pp. 17861794.
    24. 24)
      • 16. Tang, X., Deng, L., Liu, N., et al: ‘Observer-based output feedback MPC for T-S fuzzy system with data loss and bounded disturbance’, IEEE Trans. Cybern., 2019, 49, (6), pp. 21192132.
    25. 25)
      • 1. Sun, H., Peng, C., Wang, Y., et al: ‘Output-based resilient event-triggered control for networked control systems under denial of service attacks’, IET Control Theory Appl., 2019, 13, (16), pp. 25212528.
    26. 26)
      • 30. Ji, H., Zhang, H., Cui, B.: ‘Finite-dimensional guaranteed cost sampled-data fuzzy control of Markov jump distributed parameter systems via T-S fuzzy model’, IET Control Theory Appl., 2018, 12, (15), pp. 20982108.
    27. 27)
      • 3. Tian, E., Wang, Z., Zou, L., et al: ‘Chance-constrained H control for a class of time-varying systems with stochastic nonlinearities: the finite-horizon case’, Automatica, 2019, 107, pp. 296305.
    28. 28)
      • 2. Cong, Y., Zhou, X., Kennedy, R.: ‘Finite blocklength entropy-achieving coding for linear system stabilization’, IEEE Trans. Autom. Control, 2021, 66, (1), pp. 153167.
    29. 29)
      • 7. Wang, J., Ding, B., Hu, J.: ‘Security control for LPV system with deception attacks via model predictive control: a dynamic output feedback approach’, IEEE Trans. Autom. Control, 2021, 66, (2), pp. 760767.
    30. 30)
      • 13. Köhler, J., Müller, M., Allgöwer, F.: ‘Nonlinear reference tracking: an economic model predictive control perspective’, IEEE Trans. Autom. Control, 2019, 64, (1), pp. 254269.
    31. 31)
      • 31. Du, Z., Kao, Y., Park, H.: ‘New results for sampled-data control of interval type-2 fuzzy nonlinear systems’, J. Franklin Inst., 2020, 357, (1), pp. 121141.
    32. 32)
      • 37. Li, L., Quevedo, D., Dey, S.: ‘SINR-based DoS attack on remote state estimation: a game-theoretic approach’, IEEE Trans. Control Netw. Syst., 2017, 4, (3), pp. 632642.
    33. 33)
      • 19. Sun, Z., Dai, L., Xia, Y., et al: ‘Event-based model predictive tracking control of nonholonomic systems with coupled input constraint and bounded disturbances’, IEEE Trans. Autom. Control, 2018, 63, (2), pp. 608615.
    34. 34)
      • 12. Rotondo, D., Sánchez, H., Puig, V., et al: ‘A virtual actuator approach for the secure control of networked LPV systems under pulse-width modulated DoS attacks’, Neurocomputing, 2019, 365, pp. 2130.
    35. 35)
      • 15. Parisio, A., Rikos, E., Glielmo, L.: ‘A model predictive control approach to microgrid operation optimization’, IEEE Trans. Control Syst. Technol., 2014, 22, (5), pp. 18131827.
    36. 36)
      • 5. Suhaib, M., Chen, P., Zain, A.: ‘Event triggered multi-agent consensus of DC motors to regulate speed by LQR scheme’, Math. Comput. Appl., 2017, 22, (1), pp. 1425.
    37. 37)
      • 39. Li, H., Pan, Y., Shi, P., et al: ‘Switched fuzzy output feedback control and its application to a mass-spring-damping system’, IEEE Trans. Fuzzy Syst., 2016, 24, (6), pp. 12591269.
    38. 38)
      • 27. Han, H., Zhang, X., Zhang, W.: ‘Robust distributed model predictive control under actuator saturations and packet dropouts with time-varying probabilities’, IET Control Theory Appl., 2016, 10, (5), pp. 534544.
    39. 39)
      • 23. Tarbouriech, S., Garcia, G., Manoel, G., et al: ‘Stability and stabilization of linear systems with saturating actuators’ (Springer, London, UK, 2011).
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