This paper presents a novel teleoperation controller for a nonlinear master–slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the passivity based architecture upon the earlier work of Lee and Spong (2006) [14] to improve position tracking and consequently transparency in the face of disturbances and environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitations of a PD controller and guarantee an improved with genetic algorithm is investigated. We wanted to build on the controller can be designed as desired, and the optimal coefficients are obtained. پرونده مقاله

One of the many types of wind turbines that are widely used in the world, Wind turbines are powered by induction generators. Due to the increasing use of this generator in wind farms and power grids as well as the increasing use of PACIS devices and compensators in the transmission lines, a careful study of behavior and their dynamic modeling is felt more and more. To this end, a small wind turbine signal model with a dual-induced induction generator is first introduced it is introduced at different wind speeds. Then using the series compensator capacitor, dynamic behavior of double induction generator in the presence of this compensator at various wind speeds, the analysis and power stability of the transmission line are investigated. پرونده مقاله

:Thedevelopment of ICS 4.0 industry-specific cybersecurity mechanisms can reduce the vulnerability of systems to fire, explosion, human accidents, environmentaldamage, and financial loss. Honeypots are computer systems that are deployed expressly to trick attackers into thinking they are real computers. Given that vulnerabilities are the points of penetration into industrial systems, and using these weaknesses, threats are organized, and intrusion into industrial systems occurs. As a result, to learn about an attacker's behavior, tactics, strategies, and signatures, the EIDS is used to collect information on cyber-attacks, proving it to be a more helpful tool than earlier traditional ways. Attacks collected by honeypot software expose the attackers' source IP addresses as well as the target host that became a victim of the assaults. This paper proposes a novel Honeypot enhanced industrial Early Intrusion Detection System (EIDS) using Machine Learning (ML). The performance of EIDS is evaluated with ML, and the experimental results show that the proposed EIDS detects anomalous behavior of the data with a high detection rate, low false positives, and better classification accuracy. پرونده مقاله