States estimation and resilient control for non-Gaussian stochastic distribution control system under sensor attacks. (English) Zbl 1532.93083
Summary: This paper studies states estimation and resilient control schemes based on model predictive control (MPC) algorithm for a class of Takagi-Sugeno (T-S) fuzzy stochastic distribution control (SDC) system subjected to sparse sensor attacks. Firstly, a T-S fuzzy model is used to approximate the dynamics of a non-Gaussian SDC system, where the outputs of the system is the output probability density functions (PDFs). Secondly, in order to estimate the states and attacks in the system, a fuzzy Luenberger observer is designed. In addition, based on the estimated states and attacks, the designed MPC resilient control method achieves a satisfied tracking performance. Finally, the feasibility of the estimation algorithm and resilient controller are verified by simulation.
MSC:
| 93B45 | Model predictive control |
| 93B35 | Sensitivity (robustness) |
| 93C42 | Fuzzy control/observation systems |
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