Logic Controller Research Articles

Design and Implementation of an Intelligent Log Diameter Grading and Sorting Line Based on Machine Vision

In order to address the challenges posed by elevated manual labor costs and limited automation in traditional log diameter grading and sorting processes, this paper centers on the design and research of an intelligent log diameter grading and sorting line utilizing machine vision. The study focuses on logs with smaller diameters located in Fujian province, China. By analyzing production requirements, the study formulates the structure of the feeding, alignment, detection, and sorting zones to fulfill sorting functions. Using the YOLOv5 model, the system achieves accurate log end face positioning, and the diameter is computed through a designated algorithm. The operational process of the system is examined, and the control logic governing the production line is elucidated. Evaluating the practical performance of the production line, the study assesses the accuracy of diameter recognition, precision in grading, and operational efficiency. The results reveal that the absolute error in diameter detection for the sorting line averages 1.12 mm, with sorting accuracy exceeding 95%. The sorting line can automatically categorize logs with diameters ranging from 60 mm to 300 mm and lengths ranging from 2 m to 6 m, achieving an annual sorting capacity of 120,000 to 130,000 cubic meters. The research findings illustrate that the system fulfills the industry’s demands for log diameter grading and sorting, thereby enhancing economic efficiency for enterprises.

Vibration Control of Car Body and Wheel Motions for In-Wheel Motor Vehicles Using Road Type Classification

In-wheel motor vehicles are gaining attention as a new type of electric vehicle due to their efficient power units located inside each wheel hub. However, they are more susceptible to wheel resonance due to the increase in unsprung mass caused by the weight of the motor. This can result in both decreased ride comfort and driving stability. To resolve this issue, in this study, we aim to apply an optimal switching controller with a semi-active actuator—a magnetorheological (MR) damper. For the implementation of the optimal switching controller, road type classification is also carried out. An acceleration sensor is used for the road type classification, and the control logics include a ride comfort controller (the linear quadratic regulator (LQR_Paved Road)) and a wheel motion controller (LQR_Off Road) for improved driving stability. For paved roads, the LQR_Paved Road control input is applied to the MR damper. However, if a road type prone to wheel resonance is detected, the control logic switches to the LQR_Off Road. During the transition, a weighted average of both the LQR_Paved Road and LQR_Off Road control input is applied to the actuator. Computer simulations are performed to evaluate the vibration control performance, including the ride comfort and driving stability on various road profiles.

A new hybrid approach based on fuzzy logic and optimization for load frequency control

A new hybrid approach based on fuzzy logic and optimization for load frequency control

Bistability and chaotic behaviors in a 4D cancer oncolytic Virotherapy mathematical model: Pspice and FPGA implementations

Oncolytic viruses (OVs) exploit characteristics of mass cells and tumor-related reaction of the body to the presence of antigen, to lyse malignant cells and modulate the tumor microenvironment. However, the effective clinical utilization of these powerful treatment modules necessitates their logical control, especially in order to prevent solid and metastatic outgrowths. Hence, it is imperative to develop methods to protect a virus from the annihilating surroundings from the bloodstream when traveling to tumor locations. Our article reports on bistability and chaotic behavior in a 4D cancer virotherapy model. We find that unstable, stable and chaotic behaviors can appear in the model when tuning some of its parameters. With the help of the chart of dynamic behaviors in parameter spaces, numerical investigations of the system’s characteristics are analyzed followed by a discussion of the obtained results. It appears that the local transition change from an invariant one-torus (IT1) to its two-torus (IT2) counterpart can be found in the system and this undergoes a Neimark-Saker (NS) change of direction. As the increasing rate of immune effector enhance, the immune cells amount increases and as a consequence, the virotherapy dynamics becomes chaotic with a pronounced Lyapunov exponent. This can result to the failure of the virotherapy due to the interference of the immune cells with viruses. We also focus our study on the deign of ad-hoc electronic and Field Programmable Gate Arrays (FPGA) implementations of the cancer virotherapy’s model, to illustrate the obtained results.

Forester: Approximate Processing of an Imperative Procedure for Query-Time Exploratory Data Analysis in a Relational Database

Query-time Exploratory Data Analysis (qEDA) is an increasingly demanding aspect of the data analysis process that entails visually and quantitatively summarizing, comprehending, and interpreting the primary characteristics of a dataset. Nowadays, an imperative procedure is popular in relational databases for EDA because it enables us to write multiple dependent declarative queries with imperative logic. As online analytical processing (OLAP) systems contain extremely large datasets, data scientists often need quick visualizations of data, using approximate processing of imperative procedures, before analyzing them in their entirety. We identify gaps in the existing techniques, in that they are unable to sample both declarative-dependent statements and control logic at the same time and perform multi-dependent sampling-based approximate processing within the permitted time in qEDA. Traditional approximate query processing (AQP) involves tuple sampling for a single query approximation and enables queries to be executed over arbitrary random samples of tables. However, available AQP methods cannot produce a further representative sample of the data distribution for the dependent statements to estimate accurately and quickly for multiple dependent statements. On the other hand, sampling control structures, like loops and conditional statements, are discussed separately, without regard to the imperative structure of statements in a procedure. In this study, we propose Forester, a novel agile approximate processing method for imperative procedures that performs imperative program-aware sampling, which includes both statements with control regions (i.e., branch and loop) and processes them approximately within the permitted time in qEDA. Our method produces more targeted samples for each relation, while maintaining the data and control flow of dependent queries and imperative logic and determining all the conditions for a relation across all the statements in the sample that guarantee the existence of relevant data for dependent data distribution. Utilizing a workload of multi-statement imperative procedures from the Transaction Processing Performance Council Decision Support (TPC-DS) database, our experiment demonstrates that Forester outperforms the existing system in sampling, producing minimum error, and improving response time.

Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.

Effect of the Degree of Hybridization and Energy Management Strategy on the Performance of a Fuel Cell/Battery Vehicle in Real-World Driving Cycles

The study utilizes open-access data to generate power demand curves for a hybrid automotive system, testing twelve configurations with three different energy management strategies and four values for the degree of hybridization (DOH), the latter representing the share of the total power of the vehicle powertrain supplied by the battery. The first control logic (Battery Main—BTM) uses mainly batteries to satisfy the power demand and fuel cells as backup, while in the other two controllers, fuel cells operate continuously (Fuel Cell Main—FCM) or within a fixed range (Fuel Cell Fixed—FCF) using batteries as backup. The results are assessed in terms of H2 consumption, overall system efficiency, and fuel cell predicted lifespan. The battery is heavily stressed in the BTM and FCF logics, while the FCM logic uses the battery only occasionally to cover load peaks. This is reflected in the battery’s State of Charge (SOC), indicating different battery stress levels between the BTM and FCF modes. The FCF logic has higher stress levels due to load demand, reducing battery lifetime. In the BTM and FCM modes, the fuel cell operates with variable power, while in the FCF mode, the fuel cell operates in a range between 90 and 105% of its rated power to ensure its lifetime. In the BTM and FCM modes, hydrogen consumption decreases at almost the same rate as the DOH increases, due to a decrease in battery capacity and a smaller amount of hydrogen being used to recharge it. In contrast, the FCF control logic results in a larger fuel consumption when the DOH decreases. In terms of FC durability, the FCF control logic performs better, with a predicted lifetime ranging from 1815 h for DOH = 0.5 to 2428 h for DOH = 0.1. The FCM logic has the worst performance, with a predicted lifetime of 800 to 808 h, being almost insensitive to the DOH variation. Simulations were performed on two different driving cycles, and similar trends were observed. Simulations taking into account fuel cell (FC) performance degradation showed an increase in hydrogen consumption of approximately 38% after 12 years. Overall, this study highlights the importance of optimizing control systems to improve the performance of fuel cell hybrid vehicles, also taking into account the component of performance degradation.

Event-driven based simulation method for security and stability control strategies of power system

Since the security and stability control strategies (SSCSs) play a pivotal role in maintaining system stability, devising the strategies accurately and reliably is of great significance. Currently, SSCSs are typically formulated according to the ‘offline simulation and online matching’ approach. However, existing commercial simulation software is difficult to simulate the dynamic triggering mechanisms and intricate control logic of SSCSs. To simulate the set of SSCSs systematically and comprehensively, this paper has proposed an event-driven approach for simulations. Firstly, the SSCS is modeled as an event-driven model containing three elements: event, event listening, and event processing. Then, on the basis of the event triggering mechanism presented in this paper, the complex control logics of SSCSs, such as the prioritization of tripping generators, are implemented. Through event-driven models of SSCSs and subsequent utilization of the event-triggered mechanism, independent and dynamic responses of SSCSs can be realized, thus avoiding interference in the simulation of multiple SSCSs. Finally, the effectiveness of the proposed method is verified based on the IEEE 39-bus system.

Fuzzy high order differentiator observer based resilient control for distributed battery energy storage systems against unbounded FDI attacks

AbstractThis paper addresses the issue of frequency recovery in distributed battery energy storage systems (BESSs) and the balancing of the state of charge (SOC) after secondary control inputs have been subjected to false data injection attacks (FDI). A fuzzy high order differentiator (FHOD) observer based distributed resilient control is introduced with the aim of achieving frequency restoration and power adaptive allocation. In order to mitigate the performance degradation caused by the chattering of traditional sliding mode observers, a high order differentiator (HOD) is employed as an observer. By introducing fuzzy control logic to optimize the coefficients of the differentiator, the coefficient of the proposed differentiator is adjusted based on the tracking error, thereby reducing the transient overshoot of the observer when the attack signal changes, which approach effectively balances the response speed and observation accuracy. The simulation outcomes demonstrate that the suggested control strategy is able to mitigate the effects caused by complex and unbounded FDI attacks on the BESSs and exhibits superior transient performance compared to traditional HOD observer in response to changes in attack signal.

Energy digital twins in smart manufacturing systems: A case study

Among core Industry 4.0 technologies, the Digital Twin represents a promising smart technology and tool that researchers are investigating to help improve energy efficiency at the shopfloor level by analyzing and optimizing energy consumption. Energy Digital Twins are a relatively new area of research, only gaining popularity in industry applications and academia recently. where a comprehensive literature review uncovered only one ‘true’ Energy Digital Twin application across published research to date. To address this research gap, an Energy Digital Twin for smart manufacturing systems was developed and evaluated in this study. In particular, the study focused on bidirectional parameter communication between the physical and the virtual part with the aim of optimizing the energy used in the manufacturing process. To address this research gap, the research objective is to create and evaluate an application of an energy optimizing Digital Twin for a Heating Tunnel. Following the definition of a Digital Twin, the research methodology and experimental setup have three major components: i) the Heating Tunnel as the physical object, ii) the digital counterpart constructed using Python to house the digital control logic and linear energy optimization feedback model, and iii) the connecting fabric, in form of a bidirectional OPC UA communication protocol. The optimization model ingests input parameters of setpoint temperature, power level, and targeted overshoot time, and after running the simulation, returns a calculated value of the required turn off temperature to the real-time heating process of the physical system. Results show that the Energy Digital Twin is effective at maintaining the maximum temperature range of the Heating Tunnel during the heating process, in addition to reducing the energy consumption and cost for all trial runs compared to the original process. Overall, the study successfully created and evaluated a functioning energy optimizing Digital Twin with bidirectional, automated feedback. The results of this research emphasize the potential impact of Energy Digital Twin applications in any manufacturing process and show the promise of future work in this realm of Energy Digital Twins.

Application of Fuzzy Logic for Robotic Arm Joint Control of an Explosive Ordnance Disposal Unit

Precise luffing of payload in mobile cranes is a crucial part of material handling and safety in industrial operations. Explosive ordnance disposal uses a bomb disposal robot to safely disable explosive devices at a safe distance. This robot is a mobile jib crane type with a gripper as an end effector instead of the typical suspension cable with a hook. The common challenge of this crane type is the arm/jib movement sensitivity with respect to tip-over stability of the crane body. This is directly influenced by the payload and constrained by the degree of freedom. This paper presents a control strategy of the joint for luffing such that the back wheels remain in contact with the surface ground and avoid bucking at any given instance of weight change in the payload. Fuzzy logic was applied to control the motor torque and luffing angle of the arm in response to the load and gripper opening size to maintain the tip-over stability margin at the highest value possible. The response curves at different configurations of the two input signals were also analyzed based on the rules set to determine its precision with respect to the expected response curve.

Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems.

Design and Implementation of DDS Signal Generator Based on FPGA

With the rapid development of semiconductor technology, the digital circuit of the control chip gradually replaces the analog circuit of the traditional signal generator, which improves the performance of the signal generator and reduces the cost of research, development and production. This paper proposes a design scheme that takes FPGA chip as the control center. The signal generator system mainly includes DAC module, FPGA module and key module. The system uses Verilog language to develop the sine wave digital signal generation and key switching frequency control logic circuit on FPGA. The frequency control word circuit in the FPGA module provides multiple frequency sine wave selection, and the look-up table circuit realizes the reading of different phases by controlling the read address of the ROM. The 14-bit high-speed DAC module converts the digital signals sent by FPGA into analog signals. The DAC module integrates low-pass filter processing to increase the smoothness of the signal. After testing, the system meets the design requirements.

Delayed resonator with multiple distributed delays – Considering and optimizing the inherent loop delay

We generalize the distributed delayed resonator (DDR) concept by incorporating the previously unutilized but inevitable feedback loop delay, thus creating a so-called multiple-delay distributed delayed resonator (MD-DDR) for complete vibration suppression. The necessary parameter tuning and control stability analysis become more involved but remain analytically solvable. Particularly, we take the operability of control parameters imposed by hardware as an explicit condition for parameter tuning and show that the loop delay (even if it is small) can lead to incomplete vibration suppression and, counter-intuitively, even no suppression if the excitation frequency exceeds a threshold value. On the other hand, such negative effects are neutralized by correcting control parameters. The loop delay is then intentionally augmented to seek enhanced performance, yielding a considerably extended operable frequency band for the desired complete vibration suppression. Furthermore, it is optimized to expedite response speed by achieving the leftmost placement of the dominant (i.e., the rightmost) characteristic root. Instead of applying a brute-force sweeping procedure as in the previous works, we analyze the dominant root locus and seek if the jump phenomenon occurs to conduct exact optimization at a low computational cost. Finally, extensive comparisons using actual experimental parameters are performed to explore various effects of the loop delay on vibration suppression and the benefits of the proposed MD-DDR in handling such issues over the conventional DDR that treats a single delay. This study fully exploits the strength of the distributed delayed control logic, moving the DDR concept closer to real applications and aiming to establish a broader design and analysis framework for the delayed resonator from a multiple-delay perspective.

Pengaruh Lingkungan Kerja Terhadap Produktivitas Karyawan Pada PT. Lonsum Balombissie Di Kabupaten Bulukumba

The influence of the work environment on employee productivity in a company makes it very possible to determine success. The work environment has a significant influence on a company, so knowledge is needed to make achievements according to the company's goals. Also, this also has several aspects for employees to meet the company's needs so that losses do not occur. Based on the results of hypothetical measurements, the work environment partially has a positive and significant effect on employee productivity at PT Lonsum Balombissie in Bulukumba Regency. In general, the most significant influence on employee performance at PT Lonsum is a healthy work environment that is in accordance with logical control.

RELAP5/Mod3.3 thermal-hydraulics characterization of the steam generator mock-up during operational transients in STEAM facility in support of the design of the DEMO WCLL BoP

The Water Cooled Lithium Lead Breeding Blanket (WCLL BB) is a key candidate for the driver blanket of the European DEMO reactor, progressing toward its Conceptual phase by the end of 2027. To assess different water and lithium-lead technologies for the WCLL BB and Balance of Plant (BoP) systems, the Water-thermal-HYDRAulic (W-HYDRA) experimental platform is under development at the ENEA Brasimone Research Centre. Among the facilities constituting the new W-HYDRA multipurpose infrastructure, STEAM is going to experimentally investigate the DEMO WCLL BoP thermal-hydraulics, focusing on the Steam Generator (SG) of the Primary Heat Transfer Systems (PHTS), to qualify its performances and suitability under its unconventional operation. The paper aims at supporting the thermal-hydraulic characterization of the Steam Generator mock-up during the sudden power variations typical of a pulsed fusion reactor. The analyzed selected scenario is the operational transient dwell-pulse-dwell, which determines high thermal cycling and correspondent high thermo-mechanical stresses on the primary side components. Two control logics with their relative drawbacks have been analyzed with a RELAP5/Mod3.3 1-D model, the first regulating the primary side average temperature, the second monitoring the minimum one. The comparison of the two systems highlighted that neither approach leads to hazardous conditions for the facility. However, while the average temperature controller is characterized by reduced thermal stresses on the components, the minimum temperature controller is characterized by higher thermal gradients for the primary loop. Both methodologies will be tested in the dedicated experimental campaign, aiming at yielding insights and evaluations concerning control strategies applicable to the DEMO reactor.

Artificial Intelligence Techniques based on aquaculture solar thermal water heating system control

The One of the most promising applications of hot water is aquaculture. Temperature is one of the most principle parameters affects aquaculture life. It can cause stress and mortality or superior environment for growth and reproduction. Shrimp is warm water aquaculture type need hot water supply. To increase pond production it is necessary to keep water temperature at 34oC. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper a control system using artificial neural network control (NNC) either adaptive fuzzy logic control (AFLC) are design to control the hot water temperature. A comparison study is applied between the performance of FLC and NNC. The performance of NNC is the best because the control design takes into consideration different variables which give an accurate output than FLC. Also this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the proposed aquaculture heating system.

Performance Improvement of Hybrid Electric Vehicle Using Supercapacitors Energy Storage System

Globally, Internal combustion (IC) engine-based vehicles are being replaced by electric vehicles to decrease fossil fuel depletion, pollution, and the effects of global warming. But in electric vehicles, the primary concerns are battery power consumption and battery life, which affects the range of the electric vehicle (EV). In this paper, a hybrid storage system comprising a battery and a supercapacitor is simulated in MATLAB software to enhance the range, duration, and performance of the EV by controlling the charging and discharging of the supercapacitor and battery and control logic is used to integrate a li-ion battery with a supercapacitor based on the state of charging of the battery and supercapacitor. with the use of a hybrid energy storage system, the range and performance of EVs has improved.

Comparison Between Fuzy Logic and PI Controls in a Speed Scalar Control of an Induction Machine.

This article compares the response of a induction machine with scalar speed PI controls and with Fuzzy logic controls. The simulation has been developed using Simulink and Matlab [1]. The operation of the motor has been verified with different speed targets in form of steps. This work allows to be seen the improvement that can be achieved by the induction machine changing controls PI into Fuzzy.

PENINGKATAN KINERJA PLTB MELALUI KENDALI FREKUENSI FUZZY LOGIC

This research explores the use of a Governor as a frequency control mechanism in a power generation system by implementing two control methods: Proportional-Integral (PI) Controller and Fuzzy Logic Controller (FLC). The objective of the study is to evaluate the dynamic frequency response of the system and compare the performance of both control methods against the permissible standard, which is within the range of 50±2% (49 Hz to 51 Hz). Through simulations of a power generation system, parameters were adjusted to achieve the target frequency, and both control methods were evaluated. The results indicate that both methods can maintain the frequency within the desired range. However, Fuzzy Logic Controller on the Governor produces a superior dynamic frequency response with a lower steady-state error percentage. The implementation of Fuzzy Logic Controller also demonstrates a faster time to reach steady state compared to the PI Controller method. This superiority suggests that FLC provides more adaptive and responsive control to load fluctuations, enhancing the efficiency and stability of the power generation system.This research provides deeper insights into optimizing frequency control on Governors, particularly through the use of Fuzzy Logic Controller. Consequently, the development of this technology has the potential to improve the performance and reliability of power generation systems, supporting the transition towards a more efficient and sustainable electric grid.