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Adaptive Quantized Control for Markov Jump Systems Against Multimode False Data Injection Attack
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DOI：10.1049/cth2.70082.png)
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Abstract
En 中文
This study addresses the adaptive dynamic quantization control problem for Markov jump systems (MJSs) subject to multimode injection attack (IA). For the controlled system, the Markov chain and multimode IA, modelled as a categorical-distributed stochastic process, are jointly characterized by a hidden Markov model. Firstly, a sliding mode controller without quantization signals is designed and a mode-dependent Lyapunov function is constructed to establish the exponential ultimate boundedness condition of the MJSs. And then, under the frame that the state signal is quantized before transmitted to controller, an adaptive dynamic quantization strategy is further proposed such that the above controller can achieve the same control performance as the non-quantized case, eliminating the need for re-designing new controller. Finally, simulation results validate the effectiveness and robustness of the proposed quantized control strategy under combined dynamic quantization and IA scenarios.
Keywords:
dynamic quantizer
injection attacks
Markov jump systems
sliding mode control
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