States estimation and resilient control for non-Gaussian stochastic distribution control system under sensor attacks

Abstract

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.