Signal difference‐based deadband control for networked control systems with variable sampling intervals

Abstract

This study investigates a signal difference-based deadband control approach as a solution to reduce data transmission in networked control systems (NCSs). A new modelling method for the NCSs with time-varying delays, time-varying sampling intervals and signal transmission deadbands is presented. The Lyapunov functional with discontinuity is exploited, which takes full advantages of the NCSs' characteristic information including the bounds of network delay (BND), the bounds of sampling interval (BSI) and the bounds of transmission deadband (BTD). In addition, it has been shown that Lyapunov functional decreases at the jump instants. Furthermore, the new stability analysis and stabilisation conditions for the NCSs are proposed, which describes the relationship of BND, BSI, BTD and the system stability. Three examples are used to demonstrate the effectiveness of the proposed methods. The simulation results have shown that the proposed approach could guarantee the system asymptotically stable and effectively reduce the data transmission in network channel.

Deadband feedback-based scheduling approach for networked control system with variable sampling period

“…Therefore, if the variable sampling period and deadband feedback can be combined into the scheduling algorithm, it will greatly improve the performance degradation caused by interference nodes in the NCS. Although in their proposed method, Liu et al (2014) also refer to deadband feedback and variable sampling period, only the upper boundary of variable sampling period is calculated, and the sampling period is not changed in real time according to the system output. At the same time, the results mainly discuss the reduction of network delay, and do not discuss other issues such as system error and output performance.…”

Improved estimation on transmission intervals and delays for networked control systems using hybrid systems tools

“…They have received great attention by their low cost in maintenance and installation and high flexibility, but many communication constraints induced by a network including variable transmission intervals, varying delays, and scheduling protocols may degrade their performance and even lead to destabilise the systems, e.g. [1][2][3][4][5][6][7].…”

Implicit disturbance rejection performance analysis of closed loop control systems according to communication channel limitations

“…On the other hand, internal noise or faults of system components can be source of unpredictable disturbance. For instance, measurement and sensor noises [4], signal transmission issues [3, 5] may disturb control systems in practice and resulting in decrease of control performance.…”