Network Control System

Network Control System (NCS) is a distributed and a real-time feedback control system where the sys- tem node situated at different geographical position exchanges state information and control informa- tion with the controller through a communication network [20]. Network bandwidth and restraint of communication mechanism such as network delay and data packet loss exist typically in network com- munication channel, which makes NCS loses invari- ability, integrality, causality and certainty [18], and due to this fact the study of NCS is more complicated and challenging.

The next step in evolution was the distributed control system (DCS) that was introduced in 1975. Both Honeywell and Japanese electrical engineering firm Yokogawa introduced their own independently produced DCSs at around the same time, with the TDC 2000 and CENTUMsystems, respectively. As the expanding needs of industrial applications pushed the limit of point-to-point control, it became obvious that the NCS was the solution to achieve remote control operations. Research in teleoperation was initiated with the concern for safety and convenience in hazardous environments, such as space projects and nuclear reactor power plants, and was made feasible only after further development of the NCS. Later, with the advent of networking technologies, easy and cheap access to the Internet (previously known as ARPANET) proved to be a boon. Friedman emphasizes the effects of the Internet in human activities in his book ―The World is Flat.‖ Further development and research in NCSs were boosted by the tremendous increase in the deployments of wireless systems in the last few years. Today, NCSs are moving into distributed NCSs , which are multidisciplinary efforts whose aim is to produce a network structure and components that are capable of integrating distributed sensors, distributed actuators, and distributed control algorithms over a communication network in a manner that is suitable for real-time applications. This paper is organized as follows (Fig. 2). Section II talks about various NCS classifications in brief. Section III presents the research trends in NCSs for the last five years. This section also includes network delay effects, resource allocation and scheduling, network security, fault-tolerant NCSs, and, finally, successful integration in NCSs. Each research field briefly presents a few examples of NCS to explain the

There are several ways to deal with the data loss in net-work communication protocols. For example, the lost data will be required to resend in the Transmission Control Protocol (TCP)/IP. However, in real-time NCSs, this TCP/IP mechanism will cause more communication delay and is not acceptable for some control systems. For the real-time data transmission in NCSs, the User Datagram Protocol/IP is widely used because of the short communication delay. Recently, there have been three main methods to deal with the control input data loss for real-time NCSs. Method 1 is that if the control input data drop, the control input is set to zero [32]. Method 2 is that if the control input data drop, the control input keeps the previous one until new data arrive [33]. Method 3 is that if the control input data drop, the control input uses the control prediction [20], [31]. These methods have advantages and disadvantages. Method 1 is simple, but the control input causes unsmooth switching, which may not be allowed in some systems, and it is very difficult to provide the desired control performance. Method 2 has a smooth switching control input, but it is hard to achieve the desired control performance. Method 3 provides the desired control performance, but it costs a little communication efficiency. In this brief, to deal with the data dropout, the following mechanism is used. In case the output data in the feedback channel drop, the following data at time t are sent from the sensor side to the controller side:

The intelligent traffic signal control system based on IP, using the "Internet of things" thinking, combined the network and traffic equipment, made traffic equipment the intelligent IP nodes in traffic control network. At the same time, each traffic facility was installed with an independent controller, so they can achieve self-control and self-monitoring. What is more, they can run according to the instructions received, can also upload their own data to the server, including sending alerts and reminders. Those IIPUs were connected with the server through the Power Line Communication (PLC) technology, which means all the traffic equipment were connected together through a group of power lines.

The purpose of adding queues into a networked control system is to reshape the random time delays into deterministic delays. As an effect, the system becomes time- invariant. One such method is proposed by Luck and Ray [3, 12]. They propose to use an observer to estimate the plant states and a predictor to compute the predictive control. The past measurement data and the control data are stored in a First-In-First-Out (FIFO) queue, defined as Q 1 and Q 2 of sizes r and α , respectively. The structure of a network control system utilizing the queuing methodology is shown in Figure 3 [1].

Problems using reinforcement learning techniques are modeled in most cases as a Markov decision process which chooses its decision according to three factors that are the system state (S), the action (A), and the (R) reward function. In the literature, there are several works based on RL, which propose strategies, that provide a state-to-action mapping to choose the most cost-effective action for a given state. The goal of those works is to find the optimal action with the greatest overall reward. These approaches are generally distinguished by the choice of the three factors listed below. For example, for the state, some choose the number of vehicles in the queues, or the waiting time, etc. Using various reward functions can be mono-objective or multi-objective.

To initialize the sensor node in the network, we need to define number of analog inputs and outputs for the node and scheduling policy of the function i.e. fixed priority, deadline monotonic or earliest deadline first. This can be achieved by calling and initializing the function ‘ttInitKernel’ e.g. writing ttInitKernel(nbrOfInputs, nbrOfOutputs, scheduling policy). Create periodic task which wakes up after every 0.01s by calling the function ‘ttCreatePeriodicTask’. To call this function we need to define different parameters which are shown below:

We are trying to develop an algorithm which will be embedded into all OS code which will have the capacity to detect Wi-Fi network with the same service set identifier popularly called SSID. SSID is an unique identifier sent with packets over Wi-Fi networks. An evil twin attack creates the fake SSID which belongs to the Wi-Fi users. The user will access the fake SSID and enter the password. The attacker eavesdrop the password and access the information. Therefore this attack is hard to find. In this study we propose an algorithm to avoid connecting to the unknown network with the same SSID. The rest of the paper is organized as follows: Section II gives an overview of the two Access Control technologies widely used in the literature. Section III compares and contrasts the two techniques bringing out the similarities and differences. Section IV reviews the literature on studies that have proposed and developed solutions for tackling evil twin attacks. Also a discussion that brings out the limitations of existing systems is given. Section V gives a brief discussion about the existing Wireshark tool and proposes a wi-fi mask tool as an extension of wireshark tool to overcome the evil twin attack. The working methodology of the proposed model is explained. Section VI concludes with summary and future research directions.

With the development of automotive industries the need of real-time communication between the different electronics units were demanded. In the 1980’s the Robert Bosch Gmbh started to develop the CAN (Controller Area Network) communication protocol for real time control in vehicle to overcome the problem of complex wiring, cost, system reliability and data interchange among different electronics subsystem [1]. Controller Area Network (CAN) is a broadcast, serial bus system for real-time control applications which operates at data rates of up to 1 Megabits per second, and has a very high level of security and error detection mechanisms. The bit – wise arbitration scheme allows a non destructive arbitration when more than one stations try to start sending a message at the same time. The non-destructive arbitration makes it possible to fulfill response times in a real - time control system with a rather low bus transfer rate [2]. This paper include implementation of control system of a car using CAN. The control system compromises of three node for engine ignition, speed and light control. The design has been implemented on CANoe/CANalyzer software which

One solution is to model the system with two neural networks in parallel whereby one network is trained a priori with a wide range of historical dynamics while the [r]

Network security at both the e-commerce and customer sites must be constantly reviewed and suitable countermeasures must be planned. The security of a site depends on the security of the internal systems and the security of external networks. The majority of security breaches on the Internet occur at the endpoints, i.e., the local network, rather than on the main “backbone” of the Internet [2]. The most commonly used e-commerce security measure is the use of access limitation procedures. These procedures can be divided into the categories of network access, data access and client access. Network access deals with enabling the system to recognize that a user exists and has usage rights. This is facilitated by employing user profiles which include passwords, user IDs and specify which parts of the mainframe database are accessible to the user. The user profile resides in the file server or mainframe side of a network transmission. At the conceptual micro/user end of the message, physical terminal restrictions should be used. When the application of user profiles on an individual basis creates an undesirable amount of overhead, some companies move to the use of heterogeneous group access. Lower-level groups of people with similar access needs get the same access allocations. This compromise position, while easier to implement, increases access risks.

ABSTRACT: there are various stakeholders having similar goals as well as policies due to which modification to the present internet is restricted to incremental updates; implementation of every novel fundamentally extraordinary innovation is beside inconceivable. To deal with this issue, the concept of network virtualization has designed as a diversifying attribute without having restriction of any system. By permitting multiple heterogeneous system structures to live together on a shared physical substrate, network virtualization gives flexibility, promotes diversity, and promises security and increased manageability. This survey present some previous work done related to above topic.

This paper proposes an adaptive neural network algorithm has strong compatibility, some noise data, not including related function and reduce the input dimension, a fast learning process, uncertainty processing and force enhancement explanation. This paper introduces the research of robust adaptive control algorithm. First introduces the improved adaptive robust control. The adaptive law can guarantee the system asymptotically stable and robust. Then, discrete time nonlinear system by the uncertainty of output correction factor, for neural network prediction, this paper proposes a robust adaptive neural network control algorithm, simulation results verify the robustness and effectiveness of the proposed control strategy.

inserting a nonferrous bolt through a threaded hole on the platform and into one of these mounting holes. This allows for the adjustment of the platform’s surface in steps of two inches starting from 6 inches above the rate table’s surface. Holes with diameters of 1 to 2 inches have been drilled at various points on the wooden support stand and along the PVC column to allow cables to be run from the rate table’s surface to the stand’s adjustable platform. This is useful when using the previously described slip ring setup. This is also useful when attempting to position sensor power sources and Raspberry PI control devices such that they interfere minimally with the magnetic field near sensors positioned on the adjustable platform.

Virtual models linking (created in th Control Web, WinCC etc.) and communication with the real stations Simatic S7-300, S7-200 is available through OPC server or through MPI interface and software PRODAVE MPI V6.0. There is possible to create models of safety-critical processes with PLC as master element of control and analyze states and potentially dangerous transitions in the process [8]. Concept of model was also used for an experiment of authorization through Trust Chains in Ad hoc Grids [9].

In which decisions are not based on any feedback scenario of congested spots in the network. These algorithms do not monitored dynamically. The algorithms behave as controller truly based on its own present knowledge of local node like bandwidth and buffers state in the system. Under these schemes we have admission control rights to control to stabilize the traffic arrival pattern.

Abstract – In this paper a control system is proposed to simultaneously control the voltage boost and output voltage of assymetrical Γ-Source Inverters . Dynamic model of the system is used to design the controllers. Voltage boost on assymetrical Γ-Source Inverters is done by voltage control of impedance network capacitor and the regulation of inverter shoot through duty cycle. Since the stability margin of the system is low due to the right half plane zero in its dynamic model, the design of fast control system is impossible therefore a nonlinear feedforward control loop is used to faster the control system performance. Linear control system is used to control the AC-side in synchrony reference frame such that the AC voltage control system regulates the output voltage by means of modulation index regulation. The proposed control system performance is investigated by simulation in MATLAB/Simulink.

area of creation of instruments of the computed designing control system of FMS on the stages of designing (Norenkov, 2004; Dembovski, 2004) not sufficiently embrace the problems of mathematical solution of option, determination of optimal coordinate positions and reliability of functioning sensitivity elements, sensory, executive mechanisms of the system; development of data ware on the base of searching models and knowledge of logical character; creation of functional diagram of automation on the base of the modernized system of hardware; problems of creation of instruments of simulation of control system of FMS.

Luminaries are complete lighting fixtures comprising of alamp, ballast, reflectors and an enclosure for all the lightingunit components. Ballasts are used to provide the startingvoltages required for lamp ignition and to regulate thecurrent flow within the lamp in order to guarantee optimaloperation. They can be either magnetic or electronic (solidstate) types, with newer installations tending towardselectronic bal-lasts due to their superior performance interms of noise and flicker. Newer ballasts also enablefluorescent fixture dimming between 1- 100%, utilizingeither analog or digital dimming. Analog dimming utilizesa control voltage

Brushless DC(BLDC) motors are widely used for many industrial applications, In view of the problem that it is difficult to tune the parameters and get satisfied control characteristics by using normal conventional PID controller. a online identification method based on Radial Basis Function(RBF) has been proposed in this paper. In this method, connection weight of neural network was revised in time according to the speed of motor and phase current, the duty cycle of pulse width modulation (PWM) was adjusted to control the speed of BLDC motor. Conventional PID and RBF neural network PID algorithm were respectively adopted to make a comparison. the control approach was validated with simulation at first and then was implemented with a DSP TMS320F28035. Matlab simulations and experiment results showed that the proposed approach has less overshoot, faster response, stronger ability of anti-disturbance than the conventional PID controller.