Industrial Control System Network Research Articles

Online Real-Time Estimation of Response Time for Periodic Messages in Controller Area Networks

The Controller Area Networks (CAN) are widely used in industrial Networked Control Systems (NCSs), such as construction machineries, hybrid vehicles, robotics, and other applications. The message response time (MRT) or communication delay is the main issue to degrade the performance of CAN-based NCSs since its exact value is time-varying and unpredictable. The online acquisition of exact MRT can be quite helpful for the delay compensation of NCSs. However, since the clocks on different nodes are asynchronous, the MRT acquisition in CAN is a challenging work. The current delay acquisition methods for asynchronous systems are not suitable for the delay compensation in CAN-based NCSs because they either increase the bus load of CAN or cannot acquire the exact MRT in real time. In this paper, we propose a novel online real-time MRT estimation method for periodic CAN messages based on the analysis of message traces on CAN bus. The proposed method can estimate the exact MRT of the received message instance in real time without increasing the bus load and can be conveniently embedded into the CAN nodes without requiring additional equipment. In order to validate the proposed method, practical experiments are carried out and the experimental results show that the proposed method can effectively estimate the exact MRT of periodic CAN messages.

Research on the Performance Test of Wired/Wireless Heterogeneous Industrial Control Network

Industrial wireless technology is another hot technology direction after the field bus and industrial Ethernet technology. Therefore, it is particularly important to evaluate the performance of wireless networks. In this paper, based on IEEE802.15.4 standard wireless network as the research object, a wireless network performance test device is developed and the testing platform is set up. By testing for industrial wireless network control system, such the throughput, packet loss rate of the token cycle, control cycle, as performance indicators are obtained. It provides the powerful basis to improve the performance and evaluation industrial wireless network.

Data Storm Monitoring System of Large Power Industrial Control Network under Non Uniform Format

In the high power industrial control network, due to the large user base, the data traffic is plodding, the data formats are inconsistent, the network data storm is difficult to monitor. A kind of high power industrial control network data monitoring and control system is designed under non-uniform formats. The network node information collector is designed, the data is collected, and the data collection result is taken as the mathematical expectation, the mathematical expectation is trained. The problem of data flow storm statistics is solved. In the system, the format conversion function is added, data storm monitoring results is taken with the format conversion, and they are stored as text format. The monitoring program reads and displays the result, the difficulties brought by the format is not unified are solved. System test results show that the system can monitor the large power industrial control network data storm, the monitoring result is precise, and it can make the formatting process fast. A size of the generated result file is 18.1%, of the original file, and can fully reflect the network data storm characteristics, the effect is perfect.

Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

Guaranteed Cost Fault-tolerant Control of Networked Control Systems with Short Output Delay and Short Control Delay Based on State Observer

Supposing that the sensor and controller nodes were time-driven and the actuator node was event-driven, the problem of integrity against sensor failures for the networked control systems with short output delay and short control delay was discussed based on observer. The state observer of the system according to the time-delay compensation strategy was designed. Then, considering possible sensor failures, an augmented mathematic model for the networked control systems based on observer was developed. In terms of the given quadratic performance index function, the integrity condition of the system was given and the designs for guaranteed cost fault-tolerant controller and observer were presented respectively by using the cooperative design approach of the controller and observer and the approach of bilinear matrix inequalities. Finally, a numerical simulation example demonstrated the conclusions are feasible and effective. The proposed control method meets the requirements in industrial networked control systems.

An open virtual testbed for industrial control system security research

Industrial control system security has been a topic of scrutiny and research for several years, and many security issues are well known. However, research efforts are impeded by a lack of an open virtual industrial control system testbed for security research. This paper describes a virtual testbed framework using Python to create discrete testbed components including virtual devices and process simulators. The virtual testbed is designed such that the testbeds are inter-operable with real industrial control system devices and such that the virtual testbeds can provide comparable industrial control system network behavior to a laboratory testbed. Two virtual testbeds modeled upon actual laboratory testbeds have been developed and have been shown to be inter-operable with real industrial control system equipment and vulnerable to attacks in the same manner as a real system. Additionally, these testbeds have been quantitatively shown to produce traffic close to laboratory systems.

Study of Predictive Control in Industrial Networked Control System

In order to improve the performance of networked control system which has the characteristic of uncertain time delay, predictive control is adopted. The model of networked control system is analyzed. Take into account uncertain network time delay caused by various factors and problem of model mismatch, predictive controller is designed and simulation model of system is built using software of Matlab. A method of reducing model mismatch is proposed. Study results show that predictive control can make networked control system with good performance.

Multi-agent Platform and Toolbox for Fault Tolerant Networked Control Systems

Industrial distributed networked control systems use different communication networks to exchange different critical levels of information. Real-time control, fault diagnosis (FDI) and Fault Tolerant Networked Control (FTNC) systems demand one of the more stringent data exchange in the communication networks of these networked control systems (NCS). When dealing with large-scale complex NCS, designing FTNC systems is a very difficult task due to the large number of sensors and actuators spatially distributed and network connected. To solve this issue, a FTNC platform and toolbox are presented in this paper using simple and verifiable principles coming mainly from a decentralized design based on causal modelling partitioning of the NCS and distributed computing using multi-agent systems paradigm, allowing the use of agents with well established FTC methodologies or new ones developed taking into account the NCS specificities. The multi-agent platform and toolbox for FTNC systems have been built in Matlab/Simulink environment, which is in our days the scientific benchmark for this kind of research. Although the tests have been performed with a simple case, the results are promising and this approach is expected to succeed with more complex processes.