State Feedback Stabilization Over Finite‐State Fading Channels

Abstract

This paper studies state feedback stabilization over finite-state fading channels, where the stochastic characteristic of time-varying fading channels is assumed to be driven by a finite-state random process. The finite-state process is used to represent different channel fading amplitudes and/or to model different configurations of the overall physical environment. A series of necessary and sufficient conditions are given for stabilization over finite-state Markov fading channels. In the case of finite-state independent and identially distributed fading channels, explicit network requirements for stabilization are presented for both single-input case and multi-input case. It is shown that our results cover some existing results as special cases. Finally, the results are applied to stabilization of a single-input plant over a Gilbert-Elliott channel demonstrated by numerical simulations.

Genetic-Algorithm-Assisted Sliding-Mode Control for Networked State-Saturated Systems Over Hidden Markov Fading Channels

“…The transition probability matrix is defined as Π [π ij ] i,j∈Θ . In practical application, the network modes ς(k) correspond to different channel fading amplitudes or different configurations of the overall physical environment (e.g., positions of mobile agents) [26], [27], [38]. Now, at the network mode ς(k), the network input-output behavior can be characterized by…”

“…Up to now, the filtering and stabilization problems over FSMFCs have been addressed in some pioneering work, see e.g. [26], [38], [40]. From the viewpoint of practical applications, a key problem in employing the FSMFCs model is how to estimate the actual network mode based on the measured network information.…”

“…From the viewpoint of practical applications, a key problem in employing the FSMFCs model is how to estimate the actual network mode based on the measured network information. In [38], an estimation/observation model has been proposed for the actual network mode that covers three special cases, namely, the current network mode observation, one-step delayed network mode observation and no observation/estimation of network mode. Motivated by the work in [38], a seemingly interesting question is whether it is possible to develop a more general yet simple estimation model to not only cover the above three special cases but also observe the multi-step delayed network mode, and one of the motivations of this paper is therefore to answer such a question.…”

Decentralized dynamic event‐triggered control for networked control systems under unreliable communication network and actuator saturation

“…Because the signals in NCSs are transmitted through the network, some special physical phenomena will inevitably occur, such as reflection, refraction, diffraction and so on, which will lead to channels fading. Up to now, there has been some research on the control of fading signals [16,[26][27][28][29]. Moreover, the network security of NCSs is also a key point worthy of attention.…”

Static output feedback sliding mode control under rice fading channel: an interval type‐2 fuzzy modelling method

“…For example, the fading channels in [2] were modelled as multiplicative noise form covering the packet dropout as a special case. The finite-state Markovian process/chain was employed in [3,4] to model fading channels with different network states. Another prevalent model is the so-called Rice fading model [5][6][7], which characterises the effect of data dropout, channel fading, and time-delays simultaneously.…”