Multi Controller Research Articles

A Review of Control Automatically Water Irrigation Canal Using Multi Controllers and Sensors

A Review of Control Automatically Water Irrigation Canal Using Multi Controllers and Sensors

DDoS Protection System for SDN Network Based on Multi Controller and Load Balancer

DDoS attacks on SDN networks can create a single point of failure that has the potential to disrupt the overall network performance. In a single controller scheme, there is a potential risk of experiencing buffer overload, leading to traffic congestion as switches must wait for responses from the controller before forwarding network packets. To address this challenge, this research implements security measures using a multi-controller and load balancer approach, aiming to enhance SDN network resilience against DDoS attacks. The system operates by distributing the workload from the main controller to a backup controller through a load balancer when indications of a DDoS attack are detected. These attack indications are determined based on the miss rate value of unique forwarding requests exceeding a specific threshold. The results of this approach have proven effective in improving the reliability, responsiveness, and quality of SDN network traffic during DDoS attacks. The testing parameters involved in this research include controller response time and network traffic quality, comprising latency, bandwidth, throughput, and jitter. Based on the test results, the multi-controller and load balancer-based approach successfully enhanced network quality and controller responsiveness by 66.51% compared to the longer single controller scenario, specifically 202.49% during DDoS attacks. In terms of controller responsiveness, there is a very slight increase of around 0.01% in latency between the two. While Multi Controller demonstrated a remarkable 43.21% increase in throughput compared to Single Controller, this improvement in throughput is accompanied by a significant 204% increase in jitter.

Multi Controller Placement Optimization in Software Defined Network Controllers by using Genetic Algorithm

SDN stands for software-defined network, and it gives scalability to manage the network by giving the concept of centralization and open control. The key concept of software-defined networks is that it separates the control mechanism and data mechanism, so the controller of SDN is only responsible for controlling the network functions. The controller can be programmed or decoded as per the need of the network administrator, whether the network administrator wants to expand the network or downscale the existing network. Open flow is the protocol which is used to build communication between controllers and a switching node. Open flow protocol is responsible for synchronizing and receiving the flow table when the request from the switches increases to control the level and multiple routing information comes to the controller. The controller could not be able to manage such an overflow of information. As a result, the performance of the controller was affected. In this paper genetic algorithm technique is used to resolve the load balancing issue between the multiple controllers. The proposed solution of algorithm will perform the method of selection, crossover, and mutation techniques to optimize the load balancing of controllers. The proposed technique will be compared with some benchmark algorithms, and it will be proved that the proposed GA outperforms as compared to other existing algorithms. By optimizing network performance and resource utilization, the research contributes to building resilient infrastructure and promoting sustainable industrialization.

Design of fractional order multistage controller for frequency control improvement of a multi-microgrid system using equilibrium optimizer

Design of fractional order multistage controller for frequency control improvement of a multi-microgrid system using equilibrium optimizer

Load Balancing Strategy of Power Communication Network Based on SDN Controller

A load balancing strategy based on SDN controller is proposed to solve the load and security problems caused by the large number of services carried by power communication network in this article. Firstly, integrated with the actual situation of the network, the basic framework of gradual hierarchical transformation of power communication network is put forward. Then, the SDN controller response delay model is discussed in detail in the modeling process, and a multi controller deployment model and its solution algorithm are also proposed. The simulation results verify the effectiveness of the SDN centralized control function, and optimize the load balance, QoS and resource utilization, so as to reduce the impact of power grid communication link failure on the control delay.

Implementation of a SDN Architecture Observer: Detection of Failure, Distributed Denial-of-Service and Unauthorized Intrusion

Software-defined networking was recently introduced and proposed to separate the control from the data plane. This architecture introduces new challenges, particularly with regard to security and safety. To address the safety challenges, it is necessary to set up a multi controller architecture to provide redundancy. In addition, the second controller can have a security benefit because it can be used to validate the decisions taken by the first controller. However, communication between the controllers is necessary in these architectures, which may be exploited by an attacker to spread across the controllers, resulting in a security issue. This study aims to develop a multi controller architecture without communication between controllers. The control is executed by the nominal controller, which performs the data plane computation, whereas the second controller is in charge of verifying the consistency of the controller’s decisions, i.e., the management traffic. We first formulated the activity of the command and then provided conditions to determine a consistent control. These conditions include a time boundary, which corresponds to the tolerance for a delay in the response time of the controller, and structural properties to verify the consistency of the path setup. Moreover, we proposed a detection algorithm that is divided into two parts: first, a learning phase that aims to learn the consistent path set up by the controller, and second, a running phase which aims to verify that the controller sets up paths that are similar to the learned path. This algorithm was evaluated in terms of its reactivity, precision, and recall. To evaluate this, we considered three use cases: a distributed denial of service (DDOS) attack, an attack to send malicious packets on the network, and a failure of the controller.

Modeling and analysis of brushless DC motor system based on intelligent controllers

The constant innovative advancements of brushless DC motors (BLDCMs) have discovered a wide scope of utilizations. For instance, underground electric vehicles, drones, and submerged bikes have just confirmed elite BLDCMs. Be that as it may, their appropriation requires control frameworks to screen torque, speed and other execution attributes. This paper presents numerous brilliant controllers structure and order line programming to assemble, alter and mimic intelligent to control BLDCMs. Recently planned graphical user interface (GUI) structure for Multi controllers: conventional proportional integral derivative (PID) controller, intelligent controller based fuzzy techniques: type 1, type 2, and modified type 2 fuzzy logic controller (T1FLC, IT2FLC, and MIT2FLC respectively). Different phases of the problematic framework configuration process, from the underlying depiction to the last usage, can be acquired from the altered tool compartment. MATLAB Ver.2019b was utilized to mimic and plan all procedure GUI. The agreeable aftereffects of MIT2FLC have been approved also; the examination gives the subtleties of the GUI program through estimations, shapes, flowcharts and code, giving understanding into the presumptions of common-sense boundaries that may emerge in such a minimal effort mentor for BLDCM. At last, the outcomes acquired through a few reproduction tests affirm the legitimacy of the showed math model and designing of intelligent controllers.

Development of ARM and CPLD Based Multi Controller Board

Over the course of the last ten years, the number of researchers, academicians, and industry people participating in development boards has significantly increased. In order for product development departments to realize their objective of generating higher-quality items, it is vital for them to integrate the processes of developing both hardware and software. The fundamental concept that supports a multi-controller board which incorporates ARM and CPLD cores has been materialised into a working product. There is not yet a version of a Multi-Controller Boards that is currently available in the marketplace that incorporates both CPLD and ARM into such a single board at the same time. This is the case despite the fact that such a board would be desirable. Creating a board that can accommodate many controllers is the primary objective of this endeavor. In addition to a switching circuit, this board will feature an ARM chipset as well as a CPLD chipset as its primary components. In order to transition between the two chipsets, the switching circuitry have been utilized.

Multi-stage Fractional-order Controller for Frequency Mitigation of EV-based Hybrid Power System

This work reports load-frequency-control (LFC) of a three-area hybrid power system incorporating renewables and electric-vehicles (EV). Such a power system with applicable non-linearities necessitates a resilient controller. In this respect, a multi-stage fractional-order proportional-integral-derivative (MSFOPID) controller is explored for the LFC operation of the presented hybrid power system. For the proposed controller, there is a considerable improvement of 81.3% in terms of objective function (ISE value). Similarly, the foible evidence study shows an improvement of 39% with respect to other considered secondary controllers. The active role of EVs in preserving the system dynamics is inspected thoroughly. The impact of redox-flow batteries as an energy storage device and parametric variation of phase-locked-loop for system dynamic control are illustrated. Furthermore, the robustness of explored MSFOPID controller optimized gains is checked against alterations in power-system-loading and renewable energy sources, respectively.

Optimal Cascade Non-Integer Controller for Shunt Active Power Filter: Real-Time Implementation

Active power filters (APFs) are used to mitigate the harmonics generated by nonlinear loads in distribution networks. Therefore, due to the increase of nonlinear loads in power systems, it is necessary to reduce current harmonics. One typical method is utilizing Shunt Active Power Filters (SAPFs). This paper proposes an outstanding controller to improve the performance of the three-phase 25-kVA SAPF. This controller can reduce the current total harmonic distortion (THD), and is called fractional order PI-fractional order PD (FOPI-FOPD) cascade controller. In this study, another qualified controller was applied, called multistage fractional order PID controller, to show the superiority of the FOPI-FOPD cascade controller to the multistage FOPID controller. Both controllers were designed based on a non-dominated sorting genetic algorithm (NSGA-II). The obtained results demonstrate that the steady-state response and transient characteristics achieved by the FO (PI + PD) cascade controller are superior to the ones obtained by the multistage FOPID controller. The proposed controller was able to significantly reduce the source current THD to less than 2%, which is about a 52% reduction compared to the previous work in the introduction. Finally, the studied SAPF system with the proposed cascade controller was developed in the hardware-In-the Loop (HiL) simulation for real-time examinations.

An improved control strategy for an industrial copper solvent extraction process

ABSTRACT In this paper, to solve the constraints and upgrade the control systems of industrial copper solvent extraction processes, an improved two-layer (optimization and stabilization) control strategy was proposed. The proposed control strategy is equipped with an advanced–step multistage nonlinear model predictive controller. It was successfully tested against simulated disturbances, noise and set point changes. Moreover, the proposed strategy was applied for an industrial process and the obtained results were compared to the plant control strategy and the latest proposed control strategy for industrial copper solvent extraction in the literature. With the proposed control strategy and the latest proposed control strategy in the literature, the production of the copper solvent extraction process was increased by almost 6% and 3%, respectively, compared to the plant control strategy, and the process variation was decreased by 90–92% and 80–85%, respectively.

Wireless Networked Optimal Controllers for Multiple Plants

The wireless communication technologies nowadays are the most common ubiquitous innovations used in our life. It is much easier to run than wired one, and its applications are increasing steadily. It is now extensively utilized in wireless equipment controlling during the continuous development. That is, from a reasonable distance, we can monitor and control our electronics and electrical equipment. It can be said that tools can only be managed with a controller in hand from a distance without being close to the controller. One of the popular technology requirements in the present time is wireless technology. In this article, the proposed multi plant multi controller (MPMC) system is based on wireless sensor networks (WSNs). The aims are to design and implement of wireless closed-loop optimal control system for multiple plants. The system consists of two main parts: the station part and the sensing part. The station part is designed for the purpose to control the system through the graphical user interface (GUI) designed in the LabVIEW program that gives signals to the sensing part wirelessly through the ZigBee nodes. The sensing part receives a signal from the station part wirelessly through the ZigBee node and used a wireless speed sensor to calculate the speed of connecting DC motors. Then, the sensing part returns the measure reading to the station side through another ZigBee node, to show the result in the GUI controlling the operations of the two plants. The work proves the feasibility of implementing the optimal MPMC systems through wireless networks using ZigBee technology.

Grasshopper optimization algorithm optimized multistage controller for automatic generation control of a power system with FACTS devices

This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.

Performance improvement on motor BLDC speed controller by using multi controller with summation technique

BLDC motor is the most widely used in the industrial world, especially in electric vehicles. With this increasing demand, a variety of research topics emerged in BLDC motors. One popular research is on BLDC motor speed control topics to maintain speed for its application, such as intelligent cruise technology in electric cars and conveyors for line assembly. However, from several existing studies, the BLDC Motor controller still uses a single controller model. The controller's output is purely from the controller without any improvement in characteristics and has a problem with the oscillating speed setpoint (error problem). In this study, the researcher proposed a combining control with the concept of summation output to handle this problem. With this concept, the control techniques used can improve each other so that better control can be produced following the control system assessment parameters. The authors used a Fuzzy Logic Controller, Artificial Neural Network (ANN), and PID, which were combined and obtained seven control systems. The results show that the control system can improve several parameters using the summation concept from the seven controllers model. It has a positive overall correlation when viewed in terms of the difference between the Error and the setpoint or MAE (Mean Absolute Error) as parameter assessment.

Learning-based Orchestrator for Intelligent Software-defined Networking Controllers

This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we present this using Machine Learning in Software-Defined Networking technology. The first task consists of extracting relevant information from the Data flow generated from a SDN and using them to learn, to predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional Neural Network model to separate the classes that we’re working on. The result was very promising the model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to researchers looking for the trade-offs between SDN performance and IA customization.

Research on SDN Multi Controller Deployment based on K-means++

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: the mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated.

Output feedback control of electrolyte flow rate for Vanadium Redox Flow Batteries

In this paper, we propose a new strategy for electrolyte flow rate control in Vanadium Redox Flow Batteries. The keystone of our approach is a novel methodology for determination of optimal reference voltage and robust feedback controller design that provides the desired transient and steady state battery performance. In addition, we introduce a hierarchical scheme of the general battery control system that significantly simplifies classification of battery control tasks and gives guidelines for multistage controller design. The research considers a kilo-watt class VRFB system operating in a wide range of loading currents (20–180 A). The proposed control algorithm allows reference trajectory tracking with high accuracy (average error less than 0.0005%), yet providing the required battery performance at twice lower flow rates as compared to the existing solutions, and thus, decreasing pump energy consumption by a factor of 3. As a result, the proposed method can be used for flow rate regulation in practical VRFB systems ensuring their stable and efficient operation under dynamic loading conditions.

Design of a Data-Driven Multi Controllers Using VRFT and Ensemble Learning

Design of a Data-Driven Multi Controllers Using VRFT and Ensemble Learning

Single and Multi-Type Controllers with Soft Computing Methods and Routing in Software Defined Network

The software defined networking topology is enabled for efficient configuration for monitoring and enhance the network performance. The multi-controller placement becomes very critical, and it can be rectified by the proposed approach. The comparison of single type and the multi controllers are discussed in this paper.The first paper is compared with the dragonfly algorithm, in this we have considered only multi POX controllers. So the latency difference depends on the iteration of the algorithm. Thus the latency is reduced in proposed algorithm. Similarly the second paper has compared with the existing algorithm of firefly, which states the multi type of controllers such as POX, NOX, BEACON and FLOODLIGHT, among that latency is calculated and compared. The selection criteria are varied according to its type. Hence the routing is carried out on the two controllers alike POX, and NOX, through ad hoc on demand distance vector routing protocol. Subsequently, the selected controllers specifically are placed and simulated in Pareto-based Optimal Controller placement (POCO) tool for the Internet 2 topology and network simulator(NS2). Finally the comparison results makes the proposed system is better than previous methods.

Single and Multi-Type Controllers with Soft Computing Methods and Routing in Software Defined Network

The software defined networking topology is enabled for efficient configuration for monitoring and enhance the network performance. The multi-controller placement becomes very critical, and it can be rectified by the proposed approach. The comparison of single type and the multi controllers are discussed in this paper.The first paper is compared with the dragonfly algorithm, in this we have considered only multi POX controllers. So the latency difference depends on the iteration of the algorithm. Thus the latency is reduced in proposed algorithm. Similarly the second paper has compared with the existing algorithm of firefly, which states the multi type of controllers such as POX, NOX, BEACON and FLOODLIGHT, among that latency is calculated and compared. The selection criteria are varied according to its type. Hence the routing is carried out on the two controllers alike POX, and NOX, through ad hoc on demand distance vector routing protocol. Subsequently, the selected controllers specifically are placed and simulated in Pareto-based Optimal Controller placement (POCO) tool for the Internet 2 topology and network simulator(NS2). Finally the comparison results makes the proposed system is better than previous methods.