Placement Control Research Articles

Anti-Adhesive Podocalyxin Expression Is Disrupted in Recurrent Implantation Failure.

Objectives: The downregulation of anti-adhesive regulatory proteins and upregulation of adhesive genes are critical for the receptive endometrium. This study was designed to determine whether switching between the anti-adhesive podocalyxin (PDX) and adhesive HOXA10 receptivity modulator occurs in the endometrium of women with recurrent implantation failure (RIF). Methods: Twenty-four patients with RIF who could not conceive for three or more cycles despite good-quality embryo transfer constituted the study group. Twenty-four patients with unexplained infertility (UEI) matched for age, BMI, and infertility duration were included in the control group. Twenty women scheduled to undergo intrauterine device (IUD) placement for birth control were included in the comparative group. Endometrial tissue was collected from patients with RIF and UEI during egg retrieval after ovarian stimulation using the GnRH antagonist protocol. In the fertile group, endometrial tissues were collected during IUD insertion. HOXA10 mRNA expression was analyzed by qRT-PCR and PDX protein expression was analyzed by ELISA. The relative expression of endometrial HOXA10 mRNA was calculated using the 2-ΔΔCt equation. Results: The relative expression of HOXA10 mRNA in the RIF group was significantly lower than that in the UEI group (p < 0.001). HOXA10 mRNA expression in the fertile group was significantly higher than that in the RIF group and was similar to that in the UEI group. PDX expression in the RIF group was significantly higher than that in the UEI group (p < 0.001). PDX expression in the fertile group was significantly lower than in the RIF and UEI groups. A negative correlation was detected between the anti-adhesive PDX protein and adhesive HOXA10 (r = -0.797, p < 0.001). Although there was a positive correlation between endometrial thickness recorded on the day of hCG administration and HOXA10 (r = 0.590, p < 0.01), endometrial thickness was negatively correlated with PDX (r = -0.529, p < 0.01). Conclusions: The failed physiological downregulation of the anti-adhesive PDX protein in patients with RIF prevented the upregulation of adhesive HOXA10 mRNA.

Workplace Design of Selected Rice Transplanters for Farm-women Operators

Background: Modern self-propelled devices consider human factors such as space constraints, control placement, line of sight, comfort and ease of operation. These factors are linked to the operator’s anthropometric and biomechanical parameters. Effective human-machine interface design can enhance productivity, comfort and safety. Methods: An anthropometric survey was conducted among 450 female agricultural workers across three zones of Kerala. The dimensions and clearances of the operator workplace for a selected transplanter were measured based on the seat index and reference points. The data were compared with the anthropometric dimensions to assess the suitability of the transplanters for women operators. Result: Based on the anthropometric data and spatial requirements, seat designs were proposed for Yanji and Redlands transplanters. Recommendations for the operator’s seat include a 39 cm backrest height, 38 cm bottom width, 18 cm open area, 40 ± 3.4 cm length, 43 cm top width, 10 cm seat cushion height and 8 cm backrest cushion height. A 35 cm diameter circular seat is recommended for the helper.

Tensor-based Feedback Control for Locally Structured High-Dimensional Streaming Data Under Limited Control Capability

Structured high-dimensional streaming data offers abundant information that are crucial for process feedback control. Nevertheless, traditional control models predominantly emphasize the global patterns of spatiotemporal correlation within responses, often neglecting the local correlation structure. This oversight can be problematic in applications where local correlations play a significant role, such as temperature control of composite plates. Additionally, these models typically fail to incorporate local patterns within the spatial influence of control variables, an essential aspect considering the location-sensitive nature of control impact. Moreover, in practice, the suboptimal uniform placement of control variables can significantly impact the effectiveness of control strategies under insufficient control resources. To address these issues, we propose a tensor-based feedback control model for locally structured high-dimensional (LSHD) streaming data under limited control capabilities. For system modeling, we employ kernel distributions to capture the local structure within (i) the response autocorrelation and (ii) the spatial impact of location-sensitive control variables. For online control, we develop a dynamic control strategy to optimize controller placement, enhancing control efficiency despite resource constraints. Finally, we validate the effectiveness of our proposed framework through simulations and a case study.

A Reliable and Load Balancing Controller Placement Method in Software‐Defined Networks

ABSTRACTSoftware‐defined networking (SDN) achieves the programmability of the control plane by separating it from the data forwarding plane to provide flexible management of the network resources. The multicontroller architecture is required to be deployed to enhance the scalability and reliability of the control plane with the network traffic growth. However, the controller placement problem (CPP) is considered an important challenge in software‐defined networking, which should be addressed. The number of required controllers and their locations are the important challenges that affect various aspects of the separated controller plane such as the performance metrics, and ability to respond to failures. Also, unappropriated subdomain partitioning of the software‐defined network by multicontrollers may cause the unbalanced distribution of controller loads resulting in the reduction of communication performance of the network. In this paper, an optimization subdomain partitioning method based on the particle swarm optimization (PSO) algorithm is presented for deploying the CPP and allocating switches to controllers. The proposed control placement method aims to minimize the cost of the network known as the number of required controllers, to minimize the maximum load imbalance between controllers, and to improve resilience against a failure between each switch and its mapping controller. The presented method is evaluated using two widely used networks from the Internet Topology Zoo such as Aarnet, Oxford, Chinanet, Interoute, and ION topologies to show the scalability of the proposed method. The results show that the proposed method achieves better performance in the required number of controllers, propagation delay, and load balancing among controllers when compared to the controller placement methods based on the Varna, clustering‐based network partition algorithm (CNPA), and K‐means. Moreover, the proposed method improves load balancing when compared to the controller placement methods based on the Varna, CNPA, and K‐means, respectively. The proposed controller placement based on the PSO outperforms nearly 20% and 17% decline in the number of required controllers in comparison with the Varna‐based heuristic controller placement method and the CNPA for different scales of topologies, respectively. Moreover, the proposed controller placement method based on the particle swarm optimization enhances the load balancing metric by nearly 6% compared to the Varna‐based controller placement method in the case of load balancing scenario in the Interoute and ION topologies, which shows the improvement of the proposed method based on the PSO compared to the Varna‐based method. Also, in the proposed controller placement method based on the PSO, the load balancing scenario outperforms the load balancing metric among the assigned controllers by nearly 14%, 22%, 13%, and 18% compared to the K‐means‐based method in the ION, Interoute, Chinanet, Oxford, and Aarnet topologies. Furthermore, the proposed method achieves nearly 9%, 5%, and 15% decline in the average propagation delay compared to the Varna‐, CNPA‐, and K‐means‐based controller placement methods for different topologies. Furthermore, the proposed scheme achieves a higher resilience against controller failures compared to the existing approaches.

Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks.

Natural disasters can severely disrupt conventional communication systems, hampering relief efforts. High-altitude tethered balloon base stations (HATBBSs) are a promising solution to communication disruptions, providing wide communication coverage in disaster-stricken areas. However, a single HATBBS is insufficient for large disaster zones, and limited resources may restrict the number and energy capacity of available base stations. To address these challenges, this study proposes a cluster deployment of tethered balloons to form flying ad hoc networks (FANETs) as a backbone for post-disaster communications. A meta-heuristic-based multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize the placement of balloons and power control to maximize target coverage and system energy efficiency. Comparative analysis with a stochastic algorithm (SA) demonstrates that MOPSO converges faster, with significant advantages in determining optimal balloon placement. The simulation results show that MOPSO effectively improves network throughput while reducing average delay and packet loss rate.

Lightweight Two-Layer Control Architecture for Human-Following Robot.

(1) Background: Human detection and tracking are critical tasks for assistive autonomous robots, particularly in ensuring safe and efficient human-robot interaction in indoor environments. The increasing need for personal assistance among the elderly and people with disabilities has led to the development of innovative robotic systems. (2) Methods: This research presents a lightweight two-layer control architecture for a human-following robot, integrating a fuzzy behavior-based control system with low-level embedded controllers. The system uses an RGB-D sensor to capture distance and angular data, processed by a fuzzy controller to generate speed set-points for the robot's motors. The low-level control layer was developed using pole placement and internal model control (IMC) methods. (3) Results: Experimental validation demonstrated that the proposed architecture enables the robot to follow a person in real time, maintaining the predefined following distance of 1.3 m in each of the five conducted trials. The IMC-based controller demonstrated superior performance compared to the pole placement controller across all evaluated metrics. (4) Conclusions: The proposed control architecture effectively addresses the challenges of human-following in indoor environments, offering a robust, real-time solution suitable for assistive robotics with limited computational resources. The system's modularity and scalability make it a promising approach for future developments in personal assistance robotics.

Effectiveness of lower tibial tunnel placement combined with internal tension relieving suture in posterior cruciate ligament reconstruction

To compare the effectiveness between lower tibial tunnel placement combined with internal tension relieving suture and simple lower tibial tunnel placement for posterior cruciate ligament (PCL) reconstruction. The clinical data of 83 patients with simple PCL injury who met the selection criteria between January 2014 and February 2022 were retrospectively analyzed. Among them, 44 patients underwent PCL reconstruction through lower tibial tunnel placement combined with internal tension relieving suture (tension relieving suture group), and 39 patients underwent PCL reconstruction through simple lower tibial tunnel placement (control group). Baseline characteristics, including gender, age, body mass index, side of injury, cause of injury, preoperative side-to-side difference (SSD) in posterior tibial translation, visual analogue scale (VAS) score, knee range of motion (ROM), Tegner score, Lysholm score, and International Knee Documentation Committee (IKDC) scores (including symptom, daily activities, and knee function scores) were compared between the two groups, showing no significant difference ( P>0.05). The operation time and intraoperative blood loss were recorded and compared between the two groups. The effectiveness was evaluated by Lysholm score, IKDC scores, Tegner score, VAS score, knee ROM, SSD in posterior tibial translation before operation and at last follow-up, the patient satisfaction at last follow-up, and the postoperative graft recovery was evaluated by MRI. There was no significant difference in operation time and intraoperative blood loss between the two groups ( P>0.05). All patients were followed up 12-60 months, and there was no significant difference between the two groups ( P>0.05). Postoperative MRI showed that the graft was in good condition, and the reconstructed PCL graft had good signal, continuity, and tension. During the follow-up, there was no complication such as re-rupture or donor site discomfort in both groups. At last follow-up, the Lysholm score, IKDC scores, Tegner score, VAS score, knee ROM, and SSD in posterior tibial translation significantly improved in both groups when compared with those before operation ( P<0.05). The changes of Lysholm score, Tegner score, IKDC knee symptom score, and SSD in posterior tibial translation between pre- and post-operation were significantly superior in the tension relieving suture group compared to the control group ( P<0.05). However, no significant difference was found between the two groups in the changes of VAS score, knee ROM, IKDC daily activities score or knee function score between pre- and post-operation, and the satisfaction score ( P>0.05). Lower tibial tunnel placement combined with internal tension relieving suture PCL reconstruction represents a more effective surgical approach for improving postoperative laxity of PCL and knee function recovery comparing to simple lower tibial tunnel placement PCL reconstruction.

HMLB: Holonic multi-agent approach for preventive controllers load-balancing in SDN-enabled smart grid

Smart grid networks present advantages like improving reliability, security, scalability, etc. However, designing an efficient communication infrastructure for smart grid networks is a great challenge. This is because of its dependency on proprietary protocols and specific vendors. Software-defined-enabled smart grid (SDN-SG) tackles this problem by incorporating diverse protocols and standards including open source platforms. One of the most important questions in Software-defined Networking (SDN) is the controller placement problem being NP-Hard in nature. Therefore, the predominant goal of this paper is to diminish the time complexity by modeling the controller placement problem based on the holonic multi-agent system. The hierarchical structure of a holonic organization improves the computational complexity through the divide and conquer mechanism. Such an idea also decreases the distributed controllers' synchronization overhead which is an issue in the realm of SDN. On the other hand, the proper functioning of the smart grid has a strict dependency on time-critical services. Accordingly, the controller placement is supposed to be a Quality of Service-aware (QoS-aware) one. Also, intermittent topology changes in the smart grid and the occasional joining and leaving of members result in an unsteady traffic pattern and dynamicity of controller load. This research is a pioneer in providing a QoS-aware and dynamic controller placement mechanism for SDN-SG. Experimental results certify the preponderance of the approach over similar ones concerning computational complexity, packet loss, controllers’ synchronization overhead, and also load-balancing overhead.

James-Stein estimator improves accuracy and sample efficiency in human kinematic and metabolic data.

Human biomechanical data are often accompanied with measurement noise and behavioral variability. Errors due to such noise and variability are usually exaggerated by fewer trials or shorter trial durations, and could be reduced using more trials or longer trial durations. Speeding up such data collection by lowering number of trials or trial duration, while improving the accuracy of statistical estimates, would be of particular interest in wearable robotics applications and when the human population studied is vulnerable (e.g., the elderly). Here, we propose the use of the James-Stein estimator (JSE) to improve statistical estimates with a given amount of data, or reduce the amount of data needed for a given accuracy. The JSE is a shrinkage estimator that produces a uniform reduction in the summed squared errors when compared to the more familiar maximum likelihood estimator (MLE), simple averages, or other least squares regressions. When data from multiple human participants are available, an individual participant's JSE can improve upon MLE by incorporating information from all participants, improving overall estimation accuracy on average. Here, we apply the JSE to multiple time-series of kinematic and metabolic data from the following parameter estimation problems: foot placement control during level walking, energy expenditure during circle walking, and energy expenditure during resting. We show that the resulting estimates improve accuracy - that is, the James-Stein estimates have lower summed squared error from the 'true' value compared to more conventional estimates.

Controller Placement Approach with Criteria Balance in SDN networks

Software Defined Network (SDN) is an emerging technology that centralizes network control and automates network management by separating the data plane from the control plane. The control plane is a set of controllers that manage the network switches. The crucial problem of positioning these controllers is known as the Controller Placement Problem (CPP). The optimal controllers positioning is very constrained and affects the different network performance parameters, such as, controller-to-switch latency, inter-controller latency, number of hops between controller and switch and inter-controller. In this paper, we study the impact of optimizing some parameters on the others, we have developed different approaches, and each considers one or two parameters. Moreover, we propose a balanced optimization approach that considers the various network performance parameters. We formulate the problem as an Integer Linear Programming model that considers all parameters using the Lexicographic Multi-objective optimization method. By ensuring a balance between all parameters, network performance is significantly improved. The obtained results reveal that our approach is more inclusive and provides very interesting results.

A new optimization framework for piezoelectric actuators location identification

The study addresses piezoelectric actuator placement for active vibration control using an open-access framework that couples ANSYS and Python. It outlines a three-stage procedure: creating a finite element model in ANSYS APDL, using Python to determine the objective function, and applying a genetic algorithm optimization to find optimal actuator locations. Numerical simulations on plates and thin-walled conical structures demonstrate the effectiveness of the approach, showing it efficiently finds optimal placements with minimal search time and energy requirements. The study provides open-access codes for the framework, enhancing its accessibility and reproducibility.

Optimization of Network Performance in Complex Environments with Software Defined Networks

Software-defined networks (SDN) have emerged as a promising approach to address the limitations of conventional networks. Its architecture can be implemented using either a single controller or multiple controllers. Although a single controller is inadequate for managing networks, deploying multiple controllers introduces the challenge of controller placement (CPP) in a network environment. To address these issues, this study presents a Software Defined Networks-Fault-Tolerant Method (SDN-FTM) where, in the event of a network failure, the SDN controller automatically reroutes traffic through an alternate, pre-configured network path, thereby maintaining uninterrupted service. The proposed SDN-FTM was tested and evaluated in real-time using Mininet simulation tools on a real-life small scale network data from tracking unit department in Walter Sisulu University (WSU), with a focus on performance measures such as latency and throughput. From the result obtained, the proposed method produced throughput and latency on Ryu with 2.15m/s and 18408m/s respectively. Furthermore, the findings indicate that Ryu controllers generally outperform OpenFlow controllers in terms of throughput, while OpenFlow controllers exhibit lower latency. The proposed method demonstrates significant improvements in network management by providing a robust solution for maintaining high network availability and performance in the presence of faults

Enhanced capacitated next controller placement in software‐defined network with modified capacity constraint

SummarySoftware‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity.

Financial Management Behavior with Locus of Control as an Intervening Variable: Impact of of Financial Knowledge and Income Level

Micro, Small, and Medium-Sized Enterprises (MSMEs) are business sectors that offer a range of goods and services and play a significant role in developing new industries as a solution to the unemployment issue. MSMEs have a huge potential to boost local economic activity and provide the majority of people with a source of income, which would improve welfare. MSMEs are still growing, but it is not slow and stagnant growth because of issues including capital constraints, a lack of utilization of information and technology, and poor financial literacy in managing company finances. The primary data for this study came from questionnaire surveys that were conducted directly. The results of the study allow for several inferences: The degree of income and financial literacy has a significant impact on where control is placed. The way people handle their money has a huge bearing on their financial knowledge. The majority of the time, people's financial management is unrelated to their degree of income. The behavior of financial management is significantly influenced by the locus of control. Financial knowledge has a significant impact on financial management behavior through the locus of control. The placement of control is substantially influenced by income and financial literacy levels. The way people handle their money has a huge bearing on their financial knowledge. The way people manage their finances is mostly independent of their income level. The behavior of financial management is significantly influenced by the locus of control. Financial knowledge has a significant impact on financial management behavior through the locus of control.

Preface

We are pleased to present the proceedings of the International Conference on Electronics, Communications, and Intelligent Science (ECIS 2024), held in Changsha, China, from May 24th to 27th, 2024. This conference serves as a platform for academics, researchers, and practitioners to discuss the latest advancements in electronics, communications, and intelligent science. We extend our heartfelt thanks to our guest editors, Professor Yang Yue from Xi’an Jiaotong University and Professor Guan Gui from Nanjing University of Posts and Telecommunications, for their invaluable contributions and work in ensuring the high quality of this publication. Their expertise has been crucial in selecting the research papers presented in this conference. Our gratitude also goes to the Xi’an Jiaotong University, whose support has been instrumental in the successful organization of ECIS 2024. Additionally, we acknowledge the distinguished speakers who have graciously shared their knowledge and insights: Professor Mohammad S. Obaidat, Life Fellow of IEEE, who delivered an enlightening keynote on a new learning automata-based controller placement technique for software-defined network systems; Professor Yang Yue, who discussed high-speed optical communications; Professor Guan Gui, who explored ubiquitous sensing and intelligent identification for 6G wireless communications; and Professor Lei Lei from Nanjing University of Aeronautics and Astronautics, who presented his research on intelligent cooperation theory of unmanned aerial vehicular swarms based on digital twins. We also express our appreciation to the workshop chairs and members, who represent various prestigious institutions, for their efforts in facilitating specialized sessions. Their participation highlights the diversity and richness of the discussions at ECIS 2024. Finally, we thank all the attendees, authors, and reviewers whose active participation and contributions have made this conference a success. We hope that the knowledge shared and the collaborations formed here will continue to drive forward innovations in electronics, communications, and intelligent science. We look forward to your continued engagement in future editions of the conference. The Conference Committee ECIS 2024 List of Conference Committees are available in this Pdf.

Adaptive quorum based scheduling and interference-free routing for edge enabled UAV assisted software-define WSN using AI

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use.

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

Model Predictive Control for Formation Placement and Recovery of Traffic Cone Robots

The challenge of effectively managing the formation and recovery of traffic cone robots (TCRs) is addressed by proposing a linear time-varying model predictive control (MPC) strategy. This problem involves coordinating multiple TCR formations within a work area to reach a target location, which is a huge challenge due to the complexity of dynamic coordination. Unlike conventional approaches, our method decomposes the formation control problem into two main components: leader TCR motion planning and follower formation tracking control. The motion planning component involves path and velocity planning to achieve leader trajectory control, which serves as a reference trajectory for the follower. The formation tracking task extends to formation control among multiple robots to achieve the traffic cone robot formation placement and recovery task. To address the TCR input limitation problem, input constraints are considered during the design process of the MPC controllers. The effectiveness and practicality of the proposed control strategy are validated through a series of numerical simulations and physical experiments with TCRs.

An assessment of nature-inspired metaheuristic algorithms for resilient controller placement in software-defined networks

Software-defined Networking (SDN) offers flexibility and programmability, making it a desirable option for modern network architecture. SDN provides numerous benefits to network administrators due to its centralized control architecture. This allows network administrators to manage and configure the network from a single location, making it easier to manage and automate network tasks. In a network of multiple controllers, the control plane’s resiliency may impact the overall system’s performance. In a controller failure scenario, switches must be reassigned to other active controllers with adequate capacity. Thus, we define a resilient controller placement (RCP) as an optimization problem. The aim is to design physically distributed and redundant controllers to manage switches with varying resilience levels. The propagation latency may increase due to the reassignment, increasing the network cost. The objective is to determine the number of controllers required, their positions, and the allocation of the network nodes to a particular controller while reducing the average propagation latency and cost in meeting the capacity constraint of the controller. Due to the wide area network (WAN) structure, four nature-inspired metaheuristic algorithms are proposed namely, simulated annealing (RCP-SA), moth-flame optimization algorithm (RCP-MFO), particle swarm optimization (RCP-PSO), and grey wolf optimization algorithm (RCP-GWO). These algorithms are evaluated on three network datasets to determine the optimum controller number and their positions. The experimental results show that RCP-GWO performs better than RCP-SA, RCP-MFO, RCP-PSO, and the Random methods.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.