Fuzzy adaptive secure tracking control against unknown false data injection attacks for uncertain nonlinear systems with input quantization

Abstract

This paper studies the problem of fuzzy adaptive secure tracking control for nonstrict-feedback nonlinear systems with unknown false data injection (FDI) attacks and input quantization. In the nonstrict-feedback nonlinear systems, the nonlinear functions contain all state variables so that the existing control methods will lead to the algebraic-loop problem. This difficulty is solved by combining the variable separation method and the properties of fuzzy basis functions. Furthermore, the tremor problem caused by the quantized input is solved by using the nonlinear function separation technique and the impact of FDI attacks is mitigated by constructing the attack compensators. On this basis, a secure tracking control strategy is proposed, which ensures the tracking error can converge to a small neighborhood of the origin and all signals of the closed-loop systems are bounded even in the presence of unknown FDI attacks. Different from the existing resilient control results for dealing with FDI attacks, this paper proposes a secure tracking control method of nonstrict-feedback nonlinear systems and also quantifies the input signal to reduce the signal transmission burden. Finally, the simulation results verify the effectiveness of the designed control algorithm.