.png){kind=icon}
.png){kind=icon}
返回
Input-to-State Stabilization for Markov Jump Systems With Dynamic Quantization and Multimode Injection Attacks
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2023.3344869.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article explores observer-based input-to-state stabilization for Markov jump systems with both dynamic input and output quantization, as well as multimode injection attacks (IAs). The Markov chain of the plant, together with the IAs described by two stochastic processes following the categorical distribution, constitutes a pair of hidden Markov models. A sufficient condition for mean-square input-to-state stability of the feedback control system is proposed utilizing a Lyapunov-type function dependent on both the mode and exponential decay rate, the S-procedure, as well as several stochastic analysis tools. Then, a two-stage approach, with which the required controller and observer gains and dynamic scaling factors can be determined successively, is developed. The scaling factors are constructed as piecewise functions, excluding the possibility of singularities involved in previous dynamic quantized control approaches. Under the zero initial condition, a greedy backtracking suboptimization algorithm is further put forward, offering an estimate of the minimum permissible upper bound of the mean-square closed-loop state for a bounded disturbance input, given a fixed exponential decay rate. Finally, a vertical lift aircraft model is applied to validate the proposed quantized observer-based control approach and suboptimization algorithm.
Keyword:
Quantization (signal)
Hidden Markov models
Markov processes
Observers
Heuristic algorithms
Upper bound
Stability criteria
Dynamic quantization
injection attack (IA)
input-to-state stability
Markov jump system (MJS)
observer-based control
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W