This research article presents a new approach for addressing the issue of packet loss and slow response in robot control cyber-physical systems (CPS). The integration of computer units and physical devices in CPS for robot control can lead to interaction between services that results in packet loss and slow response. To solve this problem, the study focuses on CPS task scheduling. A two-level fuzzy feedback scheduling scheme is proposed to adapt task priority and period based on the combined effects of response time and packet loss. This approach modifies task scheduling by identifying patterns and variations in data that indicate the presence of feedback control. The proposed method is evaluated using empirical data, which demonstrates the feasibility of the fuzzy feedback scheduling technique and support the rationality of the CPS architecture for robot control. This research highlights the importance of effective task scheduling in CPS for robot control and the potential of fuzzy feedback scheduling to improve system performance and stability under uncertainty.
TopIntroduction
CPSs are systems that integrate physical and computational components to control and monitor physical processes (Kanimozhi et al., 2022). They are used in a wide range of industries, such as transportation, healthcare, and manufacturing. Feedback control is an important aspect of CPSs, as it allows the system to adjust its behaviour based on the current state of the process being controlled (Nagarajan et al., 2022). However, the detection of feedback control in CPSs can be difficult, especially when dealing with large amounts of data (Nagajothi et al., 2022).
Cyber physical systems (CPSs) are becoming increasingly prevalent in various industries and are used to control and monitor physical processes. They are used in a wide range of industries, such as transportation, healthcare, and manufacturing (Nagajothi, Elavenil et al., 2022) However, the detection of feedback control in CPSs can be difficult, especially when dealing with large amounts of data (Sundaramk et al., 2021). Feedback control is an important aspect of CPSs, as it allows the system to adjust its behaviour based on the current state of the process being controlled.
This research aims to propose a method for detecting feedback control in CPSs through the use of big data analysis and fuzzy logic systems. By analysing the data from the CPS, this method can detect patterns and trends that indicate feedback control, thus improving the performance and reliability of CPSs (Angalaeswari et al., 2022). Additionally, this method can also be used to optimize the performance of CPSs. By identifying patterns and trends in the data, it is possible to identify areas where the system can be improved and adjust the control parameters accordingly (Merneedi et al., 2021). Furthermore, this method can also be used to detect and diagnose faults in CPSs, which can help to reduce downtime and improve the overall performance of the system (Darshan et al., 2022).
Moreover, the proposed method can also be used to improve the security of CPSs. By analysing the data from the CPS, it is possible to detect abnormal behaviour that may indicate a cyber-attack, and the fuzzy logic system can then be used to analyse the data and determine the source of the attack (Balamurugan et al., 2023). This can help to prevent future attacks and improve the overall security of the CPS. The proposed method will also present a new approach for the detection of feedback control in CPSs that can be applied to real-world scenarios. This method will take advantage of the increasing amount of data generated by CPSs and use big data analysis techniques to extract useful information from it (Velmurugan et al., 2023). Furthermore, the use of fuzzy logic systems will allow for the handling of imprecise or uncertain data which is a common problem in CPSs. The combination of big data analysis and fuzzy logic systems will provide a robust and efficient method for detecting feedback control in CPS (Subramanian et al., 2022).