Proposed Control Mechanism Research Articles

Decentralized Virtual Impedance-based Circulating Current Suppression Control for Islanded Microgrids

Parallel connected inverters in islanded mode, are getting momentous attention due to their ability to increase the power distribution and reliability of a power system. When there are different ratings of Distributed Generation (DG) units, they will operate in parallel connection due to different output voltages, impedance mismatch, or different phase that can cause current to flow between DG units. The magnitude of this circulating current sometimes can be very large and damage the DG inverters and also cause power losses that affect power-sharing accuracy, power quality, and the efficiency of the Microgrid (MG) system. Droop control, improved droop control, and virtual impedance control techniques and modifications in the virtual impedance control technique are widely used to suppress the circulating current. However, the addition of the virtual impedance to each inverter to compensate the output impedance is resistive or inductive in nature. The resistive nature of the output impedance always causes a certain voltage drop, whereas the inductive nature of the output impedance causes phase delay for the output voltage. Both problems are addressed by the proposed control mechanism in this paper. Negative resistance, along with virtual impedance, is utilized in the proposed control strategy. The output impedance is to be maintained as inductive in nature to achieve good load sharing in droop control MGs. The simulation results validate the proposed control scheme.

Grid-Connected Inverter Output Impedance Reshaping for Passivity Enhancement and Disturbance Rejection

To improve both the stability and the disturbance suppression ability of single-phase grid-connected inverters through LCL filters, this paper proposes an inverter output impedance enhancing control mechanism. The impedance enhancing control mechanism employs a virtual impedance control and a node-voltage feedforward control to ensure sufficient passivity and high amplitude of inverter output impedance over wide frequency range of interest. The proposed control mechanism is realized through combination control of a low-voltage bidirectional voltage-source-converter (B-VSC) connected at the filter capacitor branch and the main inverter. The B-VSC sustains an extra low power without sacrificing the efficiency of the inverter. Both the simulation and experimental results are finally given to verify the validity of the proposed method, which shows the performance improvement of the proposed radial-line inverter system working under variable grid conditions.

Robust Adaptive Synchronization of a Class of Uncertain Chaotic Systems with Unknown Time-Delay

In this paper, a robust adaptive control strategy is proposed to synchronize a class of uncertain chaotic systems with unknown time delays. Using Lyapunov theory and Lipschitz conditions in chaotic systems, the necessary adaptation rules for estimating uncertain parameters and unknown time delays are determined. Based on the proposed adaptation rules, an adaptive controller is recommended for the robust synchronization of the aforementioned uncertain systems that prove the robust stability of the proposed control mechanism utilizing the Lyapunov theorem. Finally, to evaluate the proposed robust and adaptive control mechanism, the synchronization of two Jerk chaotic systems with finite non-linear uncertainty and external disturbances as well as unknown fixed and variable time delays are simulated. The simulation results confirm the ability of the proposed control mechanism in robust synchronization of the uncertain chaotic systems as well as to estimate uncertain and unknown parameters.

A Distributed Control Strategy for Parallel DC-DC Converters

This letter addresses the problem of voltage regulation and balanced current sharing in a parallel connection of heterogeneous DC-DC converters sharing a common ZIP (constant impedance, constant current, and constant power) load. To this end, a distributed dynamic control approach is developed. The proposed control approach does not rely on the load profile and the number of active converters. This letter describes theoretical aspects in rigorous Lyapunov-based stability analysis, load-independent characteristic, scalability, and plug-and-play feature of the control design, and verifies the performance of the proposed control mechanism via simulation case studies in MATLAB/Simscape Electrical environment.

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Abstract The deposition process of wire and arc additive manufacturing (WAAM) is usually planned based on a bead geometry model (BGM), which represents the relationship between bead geometries (e.g. width, height) and required deposition parameters. However, the actual deposition situation may deviate from the one in which the BGM is built, such as varied heat dissipation conditions, resulting in morphological changes of deposited beads and geometrical errors in the formed parts. In this paper, a novel control mechanism for enhancing the fabrication accuracy of WAAM based on fuzzy-logic inference is proposed. It considers the geometrical errors measured on already deposited layers and deposition context to adjust deposition parameters of beads in the subsequent layer, forming an interlayer closed-loop control (ICLC) mechanism. This paper not only presents the theoretical fundamentals of the ICLC mechanism but also reports the technical details about utilizing this mechanism to control the forming height of multi-layer multi-bead (MLMB) components. A fuzzy-logic inference machine was applied as the core component for calculating speed change of bead deposition based on height error and previously applied change. In terms of validation, the effectiveness of the proposed control mechanism and the implemented controller was investigated through both simulative studies and real-life experiments. The fabricated cuboid blocks showed good accuracy in height with a maximum error of 0.20 mm. The experimental results implied that the proposed ICLC approach facilitates deposition continuity of WAAM, and thus enables process automation for robotic manufacturing.

Control the Diversity of Population with Mutation Strategy and Fuzzy Inference System for Differential Evolution Algorithm

This paper displays how to use fuzzy inference system (FIS) to control the individual uniform diversity for differential evolution algorithm (DE). DE solves nonlinear optimization problems, and a successful control mechanism for population diversity enhances the performance of DE. This study proposed a control mechanism that contains a novel mutation strategy and FIS because FIS is suitable for consecutive and hard classified inputs. The proposed control mechanism does not fix the target vector and controls the ratio of mutating toward the whole best individual by FIS. The FIS decides the F values for this novel mutation strategy. The experiments compared the winner of each evaluated functions among four uniform diversity goals (UDGs) with conventional strategies. From experimental results, the proposed method finds superior solutions to conventional mutation strategies at least 11 out of 15 evaluated functions in 10, 30, and 50 dimensions. Furthermore, not only the diversity curves confirm the control ability of FIS, but also different paths of convergence curves indicate the fast convergence and mitigation of evolutionary stagnation.

Laguerre Neural Network Driven Adaptive Control of DC-DC Step Down Converter

DC-DC step-down/buck converters are prominent part of DC power supply system. The dynamics of DC-DC step down converter are nonlinear in nature and are largely influenced from both parametric and external load perturbations. Under its closed loop operation, obtaining a precise output voltage tracking besides satisfactorily inductor current response is a challenging control objective. In this regard, this article proposes a novel Laguerre neural network estimation technique for the approximation of unknown and uncertain load function, followed by its subsequent compensation in the adaptive backstepping controller. A detailed design of the proposed estimator and adaptive backstepping controller along with closed loop asymptotic stability have been presented. Further, the proposed control mechanism is evaluated through extensive numerical simulations while subjecting the converter to input voltage, reference voltage and load resistance perturbations. Furthermore, the results are verified by testing the proposed controller on a laboratory prototype with DSP based TM320F240 controller board. The transient performance metrics such as settling time and peak overshoot/undershoot are evaluated and compared against adaptive backstepping control and PID control methods. Finally, the analysis of results reveals that the proposed control methodology for DC-DC step down converter offers a faster transient output voltage tracking with smooth and satisfactory inductor current response over a wide operating range.

Neural Network Based Prefetching Control Mechanism

An important issue incurred by users that limits the use of internet is the long web access delays. Most efficient way to solve this problem is to use “Prefetching”. This paper is an attempt to dynamically monitor the network bandwidth for which a neural network-based model has been worked upon. Prefetching is an effective and efficient technique for reducing users perceived latency. It is a technique that predicts & fetches the web pages in advance corresponding to the clients’ request, that will be accessed in future. Generally, this prediction is based on the historical information that the sever maintains for each web page it serves in a chronological order. This is a speculative technique where if predictions are incorrect then prefetching adds extra traffic to the network, which is seriously negating the network performance. Therefore, there is critical need of a mechanism that could analyze the network bandwidth of the system before prefetching is done. Based on network conditions, this model not only guides if the prefetching should be done or not but also tells number of pages which are to be prefetched in advance so that network bandwidth can be effectively utilized. Proposed control mechanism has been validated using NS-2 simulator and thus various adverse effects of prefetching in terms of response time and bandwidth utilization have been reduced.

Neural Network Based Prefetching Control Mechanism

An important issue incurred by users that limits the use of internet is the long web access delays. Most efficient way to solve this problem is to use “Prefetching”. This paper is an attempt to dynamically monitor the network bandwidth for which a neural network-based model has been worked upon. Prefetching is an effective and efficient technique for reducing users perceived latency. It is a technique that predicts & fetches the web pages in advance corresponding to the clients’ request, that will be accessed in future. Generally, this prediction is based on the historical information that the sever maintains for each web page it serves in a chronological order. This is a speculative technique where if predictions are incorrect then prefetching adds extra traffic to the network, which is seriously negating the network performance. Therefore, there is critical need of a mechanism that could analyze the network bandwidth of the system before prefetching is done. Based on network conditions, this model not only guides if the prefetching should be done or not but also tells number of pages which are to be prefetched in advance so that network bandwidth can be effectively utilized. Proposed control mechanism has been validated using NS-2 simulator and thus various adverse effects of prefetching in terms of response time and bandwidth utilization have been reduced.

Novel Control Approaches for Demand Response With Real-Time Pricing Using Parallel and Distributed Consensus-Based ADMM

This paper studies the automated demand response (DR) problem in smart grids equipped with information and communication technology networks, where power generating and consuming units can exchange information as a multiagent system (MAS), and a real-time pricing (RTP) scheme is proposed. When the communication graph among agents is connected, a novel parallel and distributed consensus-based algorithm is proposed to derive an RTP scheme to facilitate DR, and when communication uncertainties exist, a robust consensus algorithm is proposed to cease the effect of uncertainties. Next, this paper proposes a novel control mechanism to tackle the problem of disconnected communication among agents, e.g., under cyber-attacks, by employing the so-called mixed communication-broadcast control architecture where the underlying ideas are twofold. First, each area in the grid associated with a connected subgraph is controlled by a MAS to guarantee the power balance and to reach consensus on the local electric price for that area. Second, a supervisory unit observes those local electric prices to calculate the global electric price for the whole grid and then broadcasts to all units so that they can properly adjust their output powers. Simulations are carried out on the IEEE 39-bus system to validate the proposed control mechanisms.

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Compared to traditional motor controlling techniques, the modern Artificial Intelligent (AI) controllers have many advantages. During the past decades, stochastic search and heuristic optimization algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA) have effectively enhanced the performance of motor control applications. This paper presents a metaheuristic adaptive fuzzy logic-particle swarm optimization control mechanism to optimize the speed regulation of the electric current space vector-controlled BLDC motor while modelling in MATLAB Simulink environment. This is a part of the designed and developed AI controller, for stability and traction control of a four-wheel drive electric rover. To identify the behaviour of the parameters and the algorithm of the proposed controller, this developed simulation model was utilized by taking into account only the behaviour of a one BLDC motor. In the proposed TSK-PSO-FL controller, the PSO technique is utilized to define the heuristic fuzzy associative matrix and another separately dedicated zero-order fuzzy logic controller (TSK-FLC) utilized to optimize the PSO parameters online such as the swarm hyperspace (population size), inertia weights and the acceleration. Simulated test results are presented, analyzed and compared with the developed hardware test model in addition to the newly published research work. Moreover, in addition to the simulation model, the proposed TSK-PSO-FLC was tested on a 3-ph / 250W power BLDC motor in real-time operation. The analyzed test results are clearly showing that the proposed control mechanism has been optimized and enhanced the speed regulation performance of the BLDC motor significantly while increasing the frequency of the desired input trajectory.

Exclusion Radius Particle Swarm Optimizer

Function optimization is a problem that has existed since modern engineering began. With multiple variables, brute-forcing or simple regression models become unfeasible. This paper proposes a shrinking population control method for a dynamic population particle swarm optimizer. The proposed control mechanism creates an exclusion zone, where particles are periodically purged from the simulation. The selection of particles purged relies on their particle best i.e. the position where it found its personal best fitness. Particles with their particle best outside of the exclusion zone with a predetermined radius from the current global best are removed from the simulation. The purging is done periodically in stages of equal iteration counts, and the radius is shrunk by a factor after every stage. By testing 5 benchmark mathematical functions, the results show that the proposed population control mechanism achieves better final optima than only a basic particle swarm optimizer and a simple shrinking dynamic population particle swarm optimizer where the worst performing particle is removed every so often.

A dual threshold listen-before-talk for unlicensed LTE systems

The insufficient wireless frequency bands are likely to arise with the increase in mobile traffic demand. Many mobile operators suffer from a high annual increase in the cost of licensed wireless bands. Unlicensed bands present a potential opportunity for mobile operators. A single 5-GHz band with a large bandwidth provides faster transmission for each user. However, interference caused by existing wireless transmissions, such as Wi-Fi in unlicensed bands, is a serious issue in the effective utilization of unlicensed bands by mobile operators. The Listen-Before-Talk (LBT) was proposed as a promising method to ensure the coexistence of Wi-Fi and commercial mobile services such as LTE. We focus on effective control of the contention window (CW) size to maximize the operability of LBT. Control of the CW size depends on the repetition of extended clear channel assessment (ECCA). We introduce a dual threshold system that counts the repetition of ECCA. The newly developed NS-3-based unlicensed band network simulator shows the effectiveness of the proposed control mechanism. The results indicated that the advantage of the proposed method improves 4.3–7.6% than traditional LBT method. We develop a composite metric of throughput and fairness to evaluate the performance of LTE-LAA access.

A New Mobility Control Approach for Improved Route Availability in Mobile Ad Hoc Networks

Efficient routing is one of the key challenges in mobile ad hoc networks due to stringent requirements and deployment demands in many safety-critical application contexts such as disaster rescue operations and battlefields. In most cases, the autonomous nature of node mobility causes frequent link failures and critical packet loss, which affects the overall performance of the underlying routing function. In this paper, we proposed a novel mobility control mechanism that guides the mobility behavior of the ad hoc nodes that may potentially move out of range from others. Designated coordinator nodes allocated by weighted clustering algorithm periodically execute the mobility control function to predict the future trajectories of the mobile nodes in terms of their mobility states using extreme learning machine approach. Accordingly, appropriate control packets are sent by the coordinator nodes to the nodes that may potentially move out of range from others in order to modify their future mobility states. This, in turn, drives the routing function by efficiently avoiding the unexpected route breaks and packet loss, rather yielding higher route availability. The in-depth simulation results report that the proposed mobility control mechanism significantly yields better performance compared to the state-of-the-art approaches and shows robust behavior even in highly mobile tactical and safety-critical scenarios.

State and mode feedback control for discrete-time Markovian jump linear systems with controllable MTPM

In this note, the state and mode feedback control problems for a class of discrete-time Markovian jump linear systems &#x0028 MJLSs &#x0029 with controllable mode transition probability matrix &#x0028 MTPM &#x0029 are investigated. In most achievements, controller design of MJLSs pays more attention to state &#x002F output feedback control for stability, while the system cost in practice is out of consideration. In this paper, we propose a control mechanism consisting of two parts: finite-path-dependent state feedback controller design with which uniform stability of MJLSs can be ensured, and mode feedback control which aims to decrease system cost. Differing from the traditional state &#x002F output feedback controller design, the main novelty is that the proposed control mechanism not only guarantees system stability, but also decreases system cost effectively by adjusting the occurrence probability of system modes. The effectiveness of the proposed mechanism is illustrated via numerical examples.

Cyber-threats in Ukraine as a problem in conditions of geopolitical rivalry

The subject of research is cyber-threats as an instrument of geopolitical rivalry.The goal of research is to study the impact of cyber-threats on Ukrainian society in conditions of geopolitical rivalry.The research target is to consider the format of the terminological uncertainty of the cyber-threatening problem at a national level; to study the impact of diverse cyber-threats on Ukrainian society and the state; to focus attention on the proposed control mechanisms against the growing cyber-threats in the conditions of geopolitical rivalry.Research methods. In the process of the study, the following research methods were used: a system approach, a search method, synthesis and generalization, an analogical method.Methodological basis of research were the Ukrainian scientists’ works and analytical materials.The results of work. Researching the issue of geostrategic rivalry, the most of the focus is on cyber-struggle. It is found that cyber-security problems increasingly become a problem of the national level, which is traced at the speed of adoption of normative legal decisions with legitimate mechanisms of counteraction to cyber-threats, in the creation of special cyber units to secure cyber resistance and others. The article emphasizes cyber-threats that were the most resonance for the Ukrainian society and the state over recent years, and also attention was paid to the mechanisms used by the authorities to counteract. The field of application of results. The results of this research can be applied in higher educational establishments of Ukraine to teach such disciplines as National Security, Information Security, Socioeconomic Security, etc. Conclusions. Cybercrime, cyberattacks, cyberwar are a result of the formation of an information civilization that not only enables to build a more efficient and successful society, but also forms new threats to national security, leads to geopolitical rivalry. The greatest controversy unfolds in regard to cyberspace, so the state should pay more attention to the system of formation of information policy in all its aspects: to provide terminological definiteness; to promote the formation of the proper coordination of activities of various information departments related to cyberspace opportunities; to create an effective training system for special structural cyber units; to cooperate with international agencies trying to provide cybersecurity in the world.

Temperature Based Control of Ventilation System for Optimum Climate in Tomato Greenhouse

<p>Tomato has been grown successfully in highlands of Malaysia. However, the production does not meet the demand in the large market due to problem related to climate change and lack of smart control mechanism for the tomato crop to grow healthily in the micro climate. Tomato requires optimum temperature to ensure a good quality fruit production especially under greenhouse system environment. This paper presents an optimum control mechanism for ventilation system in a greenhouse to minimize the energy consumption and meet the temperature requirement for lowland tomato. The control mechanism is tested on a tomato greenhouse system located in Universiti Putra Malaysia (UPM), Serdang. The ventilation system in the greenhouse comprises of six exhaust fans and two axial fans. Results show that the proposed control mechanism on the ventilation system is able to maintain the desired temperature for lowland tomato at lower energy consumption. With the automatic control of the ventilation system, an average of 65.8% of energy consumption was saved from the greenhouse as compared to baseline energy without the control system.</p>

Consensus of delayed multi-agent dynamical systems with stochastic perturbation via dual-stage impulsive approach

This paper investigates the problem of consensus for delayed multi-agent systems with stochastic perturbation via dual-stage impulsive approach. A novel dual-stage impulsive control technique is adopted in the field of consensus of multi-agent systems. Furthermore, the effects of stochastic perturbation have been taken into consideration simultaneously in the model, which provide a more piratical framework for the consensus of delayed multi-agent systems. Parameter uncertainties are also discussed in the research. Finally, we present the simulation results to verify the correctness of the proposed control mechanism.

Optimal synchronization of non-smooth fractional order chaotic systems with uncertainty based on extension of a numerical approach in fractional optimal control problems

In this paper, a control mechanism is presented for optimal synchronization of two non-smooth fractional order chaotic systems with parametric uncertainty based on nonlinear fractional order proportional derivative (NLFPD) controller combined with optimal periodic control signals. Unlike synchronization methods based on FPID controllers, in this approach, optimal tuning of the NLFPD controller and determination of optimal periodic control signals for synchronization process are presented in the form of non-smooth fractional order optimal control (FOC) problems. Optimal periodic control signals are demonstrated as a generalized expansion in the sense of Fourier expansion that is used to accelerate the synchronization process. Using the generalization of a numerical method in nonlinear optimal control problems and the Grunwald-Letnikov(GL) fractional derivative definition, the synchronization problem based on the suggested mechanism is transformed into the form of a smooth FOC problem. By defining a base-time soft switch, a supervisory approach is added to the proposed control mechanism for desired and robust performance against parametric uncertainty in the slave system. Finally, to illustrate the proposed control mechanism, the synchronization of two identical fractional order Chua systems with simulation results is presented. The Results show that using the suggested control mechanism, the synchronization is fast and robust against parametric uncertainty in Chua slave system.

Attitude control system for a lightweight flapping wing MAV

Robust attitude control is an essential aspect of research on autonomous flight of flapping wing Micro Air Vehicles. The mechanical solutions by which the necessary control moments are realised come at the price of extra weight and possible loss of aerodynamic efficiency. Stable flight of these vehicles has been shown by several designs using a conventional tail, but also by tailless designs that use active control of the wings. In this study a control mechanism is proposed that provides active control over the wings. The mechanism improves vehicle stability and agility by generation of control moments for roll, pitch and yaw. Its effectiveness is demonstrated by static measurements around all the three axes. Flight test results confirm that the attitude of the test vehicle, including a tail, can be successfully controlled in slow forward flight conditions. Furthermore, the flight envelope is extended with robust hovering and the ability to reverse the flight direction using a small turn space. This capability is very important for autonomous flight capabilities such as obstacle avoidance. Finally, it is demonstrated that the proposed control mechanism allows for tailless hovering flight.