Publication | Open Access
A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems
485
Citations
147
References
2019
Year
Real-time ControlEngineeringIndustrial EngineeringNetworked ControlDistributed Security ControlResilient Control SystemModel-based Distributed ControlIndustrial Control SystemSystems EngineeringPower SystemsModel-based Control TechniqueMechatronicsIndustrial CpssComputer EngineeringDistributed Control SystemCyber Physical SystemsIndustrial Cyber-physical SystemsSmart GridAutomationControl TechnologyProcess ControlControl System SecurityIndustrial Informatics
Industrial cyber‑physical systems are large‑scale, geographically dispersed, life‑critical networks of sensors and actuators that demand high reliability and scalability for filtering and control. This review surveys distributed filtering and control methods for industrial CPSs modeled by differential dynamics and identifies future research challenges. The survey examines Kalman and non‑Kalman distributed filtering algorithms, cooperative control of mobile manipulators, distributed model‑predictive control, droop‑based power system controllers, and security control strategies.
Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.
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