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

Abstract

Reasonable information scheduling strategies in the networked control system (NCS) can improve the quality of service of the network, reduce the conflict of information transmission in the network, and improve the overall performance of the NCS. In order to improve the performance of the NCS, a deadband feedback-based scheduling approach for the NCS with a variable sampling period is proposed. For the NCS with multi control loops, considering the limitation of network bandwidth resources, the dynamic real-time adjustment of a multi-loop sampling period is achieved through network utilization prediction, network bandwidth configuration and sampling period calculation. Furthermore, deadband feedback scheduling is combined with a variable sampling period algorithm. Deadband is set in the sensor and controller nodes to effectively adjust the information flow of the forward channel and the feedback channel. The proposed scheduling approach can reduce the impact of network conflict and network delay on system stability, make the network resources allocated reasonably, save network data traffic, and improve the overall performance of the NCS. A NCS with five control loops is used as the simulation object and carried out by True Time toolbox. The simulation results show that the proposed scheduling approach can improve output control performance of the system, reduce integral absolute error value of the control loops, and improve network utilization. The overall control performance of the system is improved.

Scheduling strategy for networked control system based on variable sampling period and deadband feedback

“…The relative importance of each closed loop in NCS is not considered Fuzzy priority scheduling [21][22][23] The priority changes dynamically with the system output error…”

“…If satisfies Equation (), the feedback scheduling system described by Equation () converges exponentially. The detailed proof process can be found in reference 21 where is a calculated scale factor.…”

“…Aiming at the defects of the unchanged sampling period scheduling strategy, many scholars have done a lot of related research. In their study, 21 the authors designed a scheduler with variable sampling period algorithm and fuzzy feedback priority. These two schedulers are combined to obtain the optimization goal.…”

A learning-based approach to event-triggered guaranteed cost control for completely unknown nonlinear systems

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Decentralized event-triggered resilient control for a class of networked control systems under denial-of-service attacks

“…Despite the beneficial features that NCSs provide, some security issues are affecting the performance of the control system or even causing it to malfunction (Bansal and Mukhija, 2020; Guo et al, 2021; Wang et al, 2022). The development of stability analysis and controller design for cyber–physical systems, particularly networked control systems (NCSs), has increased rapidly over the past decade due to a wide variety of industrial applications (Bahreini et al, 2019, 2021; Bahreini and Zarei, 2020; Ghorbani et al, 2021; Hassani et al, 2019; Kargar et al, 2019; Li and Chen, 2019; Li et al, 2019; Saeedi et al, 2021; Tian, 2021; Torabi et al, 2021). Therefore, cyber-security issues should be considered in the analysis and synthesis of such systems.…”