A probabilistic approach to stability and stabilization of networked control systems

Abstract

In this paper, a probabilistic approach is proposed to study the modeling, stability, and stabilization problems for networked control systems (NCSs) with simultaneous random network communication delay and data dropout. Different from existing modeling approaches, the proposed NCSs model naturally captures the stochastic characteristics of the considered NCSs by taking into account the effects of the random network communication delay and data dropout. Based on the developed NCSs model, necessary and sufficient conditions of stability analysis, state, and output feedback stabilization problems are tackled. Moreover, necessary and sufficient stability and stabilization conditions are also presented for the case when the occurrence probabilities of delay and data dropout are partially known. Finally, three numerical examples are provided to demonstrate the effectiveness and the reduced conservatism of the proposed method.

Finite frequency fault detection for networked systems with access constraint

“…A class of systems termed as networked control systems (NCSs), in which large various spatially distributed sensors, controllers, and actuators are connected through a network media , has gained a great deal of attentions. The insertion of the network introduces some advantages such as decreased system wiring, simple system diagnosis, and low cost even high efficiency and flexibility . However, it also brings some new inconvenient issues, for instance, networked‐induced delay , packet losses , and communication constraint , one can see for surveys.…”

Fault detection filter design for networked control systems with time‐varying sampling periods and packet dropouts

“…The widespread use of NCSs is motivated by the advantages such as simple installation and maintenance, low cost, high reliability, etc. For NCSs, many interesting research topics have been taken into account, such as network-induced delays and packet dropouts [2][3][4][5][6], networked predictive control systems [7][8][9], scheduling [10], guaranteed cost control [11], wireless control network [12], event-triggered control design [13,14], data drift [15], active-varying sampling periods [16], quantization effects [17], communication constraints [18], and symbolic control design [19], to mention a few.…”