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6ИНФОРМАТИКА, ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА И УПРАВЛЕНИЕ
MODELING AND INVESTIGATION OF THE REGULATOR WORKING IN THE POINTWISE SLIDING MODE
Gasimov Asif Yusif-oqlu, Mammedova Afaq Tofiq
Azerbaijan Technical University.
AZ1073 Azerbaijan,pr.Dzavida 25,Baku.
Abstract. The main disadvantage of the classic sliding mode is that the control signal is made of high frequency variations. In the recommended pointwisesliding mode the frequency of the variations decreases, since the control signal makes jumps at the isolated points. At the same time, the trajectory remains in a small neighborhood of the plane of switching. In the finish mode, the trajectories that start over the sliding plane reach simultaneously the equilibrium point. It is possible to assume that these regulators will expand the implementation scope of the sliding (quazisliding) modes.
Keywords: finite mode, pointwise sliding mode, Regulator
Retention of the robust property of the system and simultaneously reducing the frequency of the control signal can provide the expansion of the application of the regimes. Considering this in the work the establishment and investigation of the "pointwise sliding mode" is discussed.
The pointwise mode differs from the classic sliding mode by the fact that here the jumps of the control signal on the line s=0 occur at the isolated points. As the time interval between these points can be adjusted, it is possible to get the following modes:
•finite mode. In this mode all trajectories are reached to the stability point at the finite
time interval as a result of single chage of the control; •Pointwise sliding mode. In this mode, the control signal is exposed to finite jumps; it is possible to increase the frequency of vibrations (as in the ordinary mode) by changing
the setting parameters [3].
•Ordinary sliding mode. In this mode, the frequency of the variation of the control signals
is closer to infinity;
•Change mode. In this mode, the changes in the value and sign of the control signal occur when the phase trajectory exceeds the line s = 0 and the ordinate axis f =0. [4].
Fig. 1, a-c shows the change of control signal in the appropriate modes and phase portraits.
Br
X Y Plot
05
и 3. 0 >-
V
•05 V /
- -05 0 0.5 1 X Axis
a)
b)
c)
Fig. 1. Characteristics of the exusting modes a) finite mode; b) pointwise sliding mode;c) ordinary mode
The regulation low for the second order linear objects is as follows
U ififj = ccmsirssi/cr < 0,
Here a = se is the variation function; s = cs + s = 0 is the variation line; or, > 0 are the the regulator strength coefficients;^ e [e: s = 0}.The setting parameters p. care defined by the spesific methodology computer simulaton.
Fig. 2 shows the Simulink scheme (a), dynamic (b) and static (c) characteristics of the regulator operating in the pointwise sliding mode.
b) c)
Fig. 2. Simulink scheme (a), dynamic (b) and static (c) characteristics for the regulator working in the pointwise sliding mode
This regulator is a robust variable structure regulator that allows reducing the frequency of the control signal in the known sliding mode [1,2].
Different from the classical methods in the pointwise sliding mode, while reducing the frequency of the control signal when the object's parameters and external inluences vary in a wide interval it becomes possible to provide movement (robustness) of the system's together with close neighbourhood of the of the system variation line. At the same time, it was prevented the occuring the autovibtations in the smallneighbourhood of the stability point.
REFERENCES
1.G.A. Rustamov. Robust control system with increased potential. News of Tomsk Polytechnic University, Vol.324, No.5, 2015, pp.13-19.
2.V.I. Utkin. Sliding Modes in Optimization and Control Problems. SpringerVerlag, NewYork, 1992. 3.S.V. Yemelyanov, S.K.Korovin. New types of feedback: Control under uncertainty. Moscow, Nauka,
1997, 352 p.
4. Gasimov A. Y., Farkhadov V. G., Yolchuyev I. A. Synthesis and implementation of controllers operating in point slidint mode. Materials of the IV International scientific-tehnical conference "Computer modelling and optimization of complex systems". Dnepr, Ukraine, 2018, pp. 149-152.
5G NETWORK THREAT ANALYSIS
Kononova N. V., Grobova S.K., Azarova E.R., Kononov M.N., Palashchenko V.N.
Abstract. This article is about analyzing threats in the network 5G. Keywords: network, threats, 5G
In the coming era of the massive 5G Internet of Things (mIoT), we are expected to have 1000 devices connected to each person, and these devices will be components of the "5G operating system" for our smart cities, smart homes, smart transportation, smart healthcare, Industry 4.0, and more [1]. To ensure this, we will need complex systems, mechanisms to maintain these devices, corporate and telecommunications data centers, public clouds. They all play a role in each of the most use cases, rotate dynamically and are accessible via virtualized networks. And the solution to these multi-criteria tasks will, of course, be complex artificial intelligence systems. This promising new world is already here, and it creates unique cybersecurity challenges that will make our past cybersecurity paradigms obsolete. 5G is not just a next-generation network, but the potential for new ways of being, new technologies and new industries. What recently existed solely in the realm of science fiction, with 5G will become possible in the real world. This technology offers a platform for integrating many different aspects of 21st-century life in creative and exciting ways [2]. It is necessary to carry out this integration correctly and in a timely manner, taking into account the issues of comprehensive security. On the one hand, the population is more globalized and integrated than before, but on the other hand, people seem to be more polarized and compressed. Finding a balance between these forces is key. Realizing 5G's full potential will require collaboration, but also smart security. There may be value in the fact that any government cares about security and privacy, but this can do more harm than good. It's time consuming, costly and distracting from the much more serious challenges inherent in 5G adoption. In 2020, many countries associated the transition to the fifth generation with the spread of the coronavirus COVID-19. There is a widespread belief that network of bots has been established in the UK and the US to promote a conspiracy theory that 5G towers are contributing to coronavirus infection, which has contributed to the massive arson of 5G towers. This absurdity has led many people to believe that 5G networks negatively affect the body and suppress the immune system, which makes it harder for a person to resist viruses.
As it rolls out, 5G is rapidly becoming the backbone for the functioning of national critical infrastructures. Previously, this was not possible with 3G or 4G, but today 5G will reallocate resources, transportation, medical services, agriculture, water supply and sanitation systems, energy, defense and many other vital sectors [3]. This network will quickly become the infrastructure on which virtually all areas of life depend - the most critical of critical infrastructures. The progress will be incredible, we will be able to experience previously unimaginable levels of efficiency. We will see the strengthening of new technologies such as autonomous vehicles and remote surgery. But as systems become more unified on critical 5G infrastructure, risks are transforming and increasing. Instead of critical infrastructure, 5G itself will become a prime target for cyberattacks.
Therefore, today it is necessary to pay close attention to 5G security, when the network is still in its infancy and the problems may not be so catastrophic.
5G networks are the next step in the evolution of mobile communications, which will provide completely new possibilities for a variety of uses on various devices in many industries. In the near future, everything will work on 5G, it will change market relations, affect the processing of information in the world in real-time. Now we can only imagine how medical procedures, financial transactions, remote industrial automation, military operations or the work of emergency services will take place. Therefore, it should come as no surprise that 5G is expected to become the most important infrastructure.
Artificial intelligence in the form of machine learning has dramatically improved internet and security and has potential for 5G. This will optimize investments and reduce costs through accurate 5G network planning, predicting capacity expansion, access to automatic coverage optimization, enabling dynamic scheduling of cloud network resources, and enabling intelligent 5G network slicing. In the coming years, artificial intelligence will help move from the existing human-based management model to a self-directed automatic management model. With this evolution, a reasonable transition will be achieved in the operation and maintenance of the network. Of course, new challenges will arise that telecommunications and cybersecurity professionals have never
