scholarly journals Cyber-Physical Industrial Control System Testbed: A Cyber-Security Solution

2020 ◽  
Author(s):  
Mohammad Nourizadeh ◽  
Mohammad Shakerpour ◽  
Nader Meskin ◽  
Devrim Unal
Keyword(s):  
Cyber Security ◽  
Process Model ◽  
Degrees Of Freedom ◽  
Industrial Process ◽  
Environmental Safety ◽  
Open Loop ◽  
Industrial Control ◽  
Systems Research ◽  
Process Plant ◽  
Real Risk

In this project, the hybrid testbed architecture is selected for the development of ICS testbed where the Tennessee Eastman (TE) plant is simulated inside PC and the remaining components are implemented using real industrial hardware. TE plant is selected as the industrial process for the developed cybersecur ity testbed due to the following reasons. First, the TE modTheel is a wellknown chemical process plant used in control systems research and it dynamics is well understood. Second, it should be properly cont rolled otherwise small disturbance will drive the system toward an unsafe and unstable operat ion. The inherent unstable open-loop property of the TE process model presents a real-world scenario in which a cyberattack could represent a real risk to human safety, environmental safety, and economic viability. Third, the process is complex, coupled and nonlinear, and has many degrees of freedom by which to control and perturb the dynamics of the process.

Developing Algorithms for a Berry Recognition System Used in Robotized Harvesting of Garden Strawberry

2020 ◽  
Vol 67 (1) ◽  
pp. 133-141
Author(s):  
Dmitriy O. Khort ◽  
Aleksei I. Kutyrev ◽  
Igor G. Smirnov ◽  
Rostislav A. Filippov ◽  
Roman V. Vershinin
Keyword(s):  
Computer Vision ◽  
Technological Process ◽  
Degrees Of Freedom ◽  
Computer Control ◽  
Recognition System ◽  
Raspberry Pi ◽  
Laser Sensors ◽  
Lighting Conditions ◽  
Systems Research ◽  
Degree Of Maturity

Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.

scholarly journals A Cyber-Security Methodology for a Cyber-Physical Industrial Control System Testbed

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 16239-16253
Author(s):  
Mohammad Noorizadeh ◽  
Mohammad Shakerpour ◽  
Nader Meskin ◽  
Devrim Unal ◽  
Khashayar Khorasani
Keyword(s):  
Control System ◽  
Cyber Security ◽  
Industrial Control System ◽  
Industrial Control

Cyber security in industrial control system

2021 ◽  
pp. 481-493
Author(s):  
Sarika Singh ◽  
Gargi Phadke
Keyword(s):  
Control System ◽  
Control Systems ◽  
Communication Networks ◽  
Cyber Security ◽  
Denial Of Service ◽  
Mitigation Measures ◽  
Security Assessment ◽  
Industrial Control System ◽  
Industrial Control ◽  
Smart Grid Communication

For any system to secure them industrial control system plays an important role in it. It helps to design the isolated procure system, specialized communication mechanisms is used to help for the setup. And with the help of this setup the flexibility, safety, threats, and vulnerabilities are the most important things to make. To secure them from risk assessment and other protection measurement need to specify with good instruments and security. The paper describes technical aspects on Denial of Service (Dos) attack. We also identify how smart grid communication networks works in security technical implementation guides of the different countries as a defense information systems agency. A brief chronicle of cyber storm on ICS; common challenges, some mitigation of those challenge, all levels of the multi-layered ICS architecture. This paper demonstrates railway control systems (RCS) compliance estimation of immovable control system design, operational scenarios that can be used for mitigation measures and security assessment.

scholarly journals A novel industrial control architecture based on Software-Defined Network

2018 ◽  
Vol 51 (7-8) ◽  
pp. 360-367
Author(s):  
Geng Liang ◽  
Wen Li
Keyword(s):  
Large Scale ◽  
Service Management ◽  
Virtual Machines ◽  
Industrial Process ◽  
Control Network ◽  
Software Defined Network ◽  
Intelligent Network ◽  
Industrial Control ◽  
Control Functions ◽  
And Performance

Traditionally, routers and other network devices encompass both data and control functions in most large enterprise networks, making it difficult to adjust the network infrastructure and operation to large-scale addition of end systems, virtual machines, and virtual networks in industrial comprehensive automation. A network organizing technique that has come to recent prominence is the Software-Defined Network (SDN). A novel SDN based industrial control network (SDNICN) was proposed in this paper. Intelligent network components are included in a SDNICN. Switches in SDNICN provided fundamental network interconnection for the whole industrial control network. Network controller is used for data transmission, forwarding and routing control between different layers. Service Management Center (SMC) is essentially responsible for managing various services used in industrial process control. SDNICN can not only greatly improve the flexibility and performance of industrial control network but also meet the intelligence and informatization of the future industry.

scholarly journals Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

2020 ◽  
Author(s):  
Sebastijan Veselic ◽  
Claudio Zito ◽  
Dario Farina
Keyword(s):  
Degrees Of Freedom ◽  
Linear Time ◽  
Open Loop ◽  
Human Robot Interaction ◽  
Manual Control ◽  
Reach To Grasp ◽  
Robot Interaction ◽  
Loop Control ◽  
User Awareness ◽  
Improving Accuracy

Designing robotic assistance devices for manipulation tasks is challenging. This work aims at improving accuracy and usability of physical human-robot interaction (pHRI) where a user interacts with a physical robotic device (e.g., a human operated manipulator or exoskeleton) by transmitting signals which need to be interpreted by the machine. Typically these signals are used as an open-loop control, but this approach has several limitations such as low take-up and high cognitive burden for the user. In contrast, a control framework is proposed that can respond robustly and efficiently to intentions of a user by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision making on how to assist the user. Context-awareness is achieved by creating a set of candidate grasp targets and reach-to grasp trajectories in a cluttered scene. User-awareness is implemented as a linear time-variant feedback controller (TV-LQR) over the generated trajectories to facilitate the motion towards the most likely intention of a user. The system also dynamically recovers from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting the user’s intention, the proposed controller allows the subject to achieve superhuman performance in terms of accuracy and thereby usability.

Two-Step Process Identification With Correlation Analysis and Least-Squares Parameter Estimation

1974 ◽  
Vol 96 (4) ◽  
pp. 426-432 ◽  
Author(s):  
R. Isermann ◽  
U. Bauer
Keyword(s):  
Correlation Analysis ◽  
Process Model ◽  
A Priori ◽  
Industrial Process ◽  
Computation Time ◽  
Small Computer ◽  
Nonparametric Model ◽  
Identification Method ◽  
On Line ◽  
Process Computer

An identification method is described which first identifies a linear nonparametric model (crosscorrelation function, impulse response) by correlation analysis and then estimates the parameters of a parametric model (discrete transfer function) and also includes a method for the detection of the model order and the time delay. The performance, the computational expense and the overall reliability of this method is compared with five other identification methods. This two-step identification method, which can be applied off-line or on-line, is especially suited to identification by process computers, since it has the properties: Little a priori knowledge about the structure of the process model; very short computation time; small computer storage; no initial values of matrices and parameters are necessary and no divergence is possible for the on-line version. Results of an on-line identification of an industrial process with a process computer are shown.

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