Nonstationary Filtering for Fuzzy Markov Switching Affine Systems With Quantization Effects and Deception Attacks
Jun Cheng, Yuyan Wu, Zheng‐Guang Wu, Huaicheng Yan
Abstract
This article focuses on the issue of nonstationary filtering for uncertain fuzzy Markov switching affine systems (FMSASs) with quantization effects and deception attacks (DAs). The resulting FMSASs are comprised of Markov switching piecewise-affine systems over a set of operating regions. To characterize the multinetwork-induced constraints, the measurement output is quantized before being transmitted, and a compensation scheme is applied to tackle the quantized measurement output loss intermittently. Meanwhile, the randomly occurring DAs are involved, in which the attack behaviors are identified by the bounded stochastic signals. Differently, to deal with the multinetwork-induced constraints, a novel nonstationary region-dependent affine filter strategy is developed. By resorting to a mode-dependent and region-dependent Lyapunov functional and S-procedure theory, sufficient conditions are elicited such that the filtering error system is mean-square exponentially stable. Finally, the practicability of the derived results is verified by a practical tunnel diode circuit model.