Flexible Control System Research Articles

Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing

This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction.

Stochastic LPV MPC-based path following control for bevel-tip flexible needle with probabilistic constraints

This paper addresses the path-tracking problem for flexible needle control systems using a stochastic linear parameter varying (LPV) and model predictive control (MPC) strategy. Flexible needles operating in dynamic environments with non-uniform tissue density often deviate from ideal assumptions, resulting in non-standard models. The bicycle kinematics model for flexible needle motion control is transformed into an LPV model, improving accuracy and enabling more efficient control. The proposed stochastic LPV MPC approach aims to mitigate uncertainties arising from modelling errors and dynamic environmental factors, ensuring accurate trajectory tracking for the flexible needle. The sample and removal method is utilized to reformulate the probabilistic-constrained optimization problem for implementation. The contributions of this work lie in the application of stochastic LPV MPC to address the trajectory tracking problem in the presence of uncertainties. The simulation results illustrate the superior robustness of the stochastic LPV MPC approach, as evidenced by significantly smaller tracking errors across various scenarios.

Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

XRL-FlexSBR: Multi-agent reinforcement learning-driven flexible SBR control with explainable performance guarantee under diverse influent conditions

The sequencing batch reactor (SBR) process stands out for its small footprint and operational flexibility. However, the SBR process is highly nonlinear and subject to influent disturbances. In this study, we suggested an explainable multi-agent reinforcement learning (XRL) approach coupled with multi-agent reinforcement learning (MARL) and explainable AI (XAI); then, an XRL-driven flexible SBR control (XRL-FlexSBR) system was developed to conduct multivariate control the SBR process autonomously. Influent big datasets including biochemical oxygen demand (BOD) and total nitrogen (TN) were collected from the wastewater treatment plants (WWTPs) of South Korea. Then, the Gaussian mixture model was utilized to cluster the diverse influent conditions and the SBR mechanistic model was developed. A game abstraction method based on a two-stage attention network (G2ANET), one of MARL algorithms, was employed to manipulate dissolved oxygen (DO) and extra carbon injection (EC) controllers in the SBR process; furthermore, layer-wise relevance propagation (LRP) of explainable AI (XAI) technique was utilized to evaluate a control performance guarantee by G2ANET. The results verified that XRL-FlexSBR can control the DO and EC controllers in the SBR process while reducing the energy consumption by 4.93 % on average and maintaining effluent quality criteria across the diverse influent conditions. Furthermore, XAI explained that the improved control performance of XRL-FlexSBR agents is attributed to their understanding of the mechanism underlying SBR operations without human intervention. Hence the proposed XRL-FlexSBR can flexibly control the SBR to improve sustainability and profitability under varying influent conditions.

Linear Active Disturbance Rejection Control for Flexible Excitation System of Pumped Storage Units

The role of pumped storage in global energy structure transformation is becoming increasingly prominent. This article introduces a flexible excitation system based on fully controlled device converters into pumped storage units (PSUs). It can address the issues of insufficient excitation capacity and limited stability associated with traditional thyristor excitation systems. The study focuses on linear active disturbance rejection control (LADRC) for the flexible excitation control system of pumped storage units and utilizes intelligent optimization algorithms to optimize the controller parameters. This addresses the inherent problem of traditional PID controllers, which are unable to alleviate the trade-off between response speed and overshoot. At the same time, the robustness and anti-interference of the control system are improved, effectively enhancing the performance of the pumped storage flexible excitation control system. Simulation verifies the feasibility and superiority of the proposed method.

Microheater Chips with Carbon Nanotube Resistors.

The specific and excellent properties of the low-dimensional nanomaterials have made them promising building blocks to be integrated into microelectromechanical systems with high performances. Here, we present a new microheater chip for in situ TEM, in which a cross-stacked superaligned carbon nanotube (CNT) film resistor is located on a suspended SiNx membrane via van der Waals (vdW) interactions. The CNT microheater has a fast high-temperature response and low power consumption, thanks to the micro/nanostructure of the CNT materials. Moreover, the membrane bulging amplitude is significantly reduced to only ∼100 nm at 800 °C for the vdW interaction between the CNTs and the SiNx membrane. An in situ observation of the Sn melting process is successfully conducted with the assistance of a customized flexible temperature control system. The uniform wafer-scaled CNT films enable a high level of consistency and cost-effective mass production of such chips. The as-developed in situ chips, as well as the related techniques, hold great promise in nanoscience, materials science, and electrochemistry.

Photoresponsive Janus microfibers array film with tunable electrical anisotropy

A new kind of photoresponsive film is produced by integrating photoconductive material into the type-II anisotropic conductive film. This film consists of orderly arranged Janus microfibers, with each Janus microfiber is comprised of a conductive region and an insulating region. Copper phthalocyanine, as the photoconductive material, is introduced into the conductive regions of these Janus microfibers, and thus the electrical conduction of the conductive regions can be enhanced by applying appropriate external light irradiation, while the insulating regions are basically unaffected. Based on this property, the electrical conduction along with the length direction of the Janus microfibers in the film is adjustable by light, the perpendicular direction along the film surface remains constantly insulating, and thus the electrical anisotropy of the resulting Janus microfibers array film can be tuned under light irradiation. The products have potential applications in flexible electronic devices and control systems.

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.

Reminiscences on the honeybee genome project and the rise of epigenetic concepts in insect science.

The sequencing of the honeybee genome in 2006 was an important technological and logistic achievement experience. But what benefits have flown from the honeybee genome project? What does the annotated genomic assembly mean for the study of behavioural complexity and organismal function in honeybees? Here, I discuss several lines of research that have arisen from this project and highlight the rapidly expanding studies on insect epigenomics, emergent properties of royal jelly, the mechanism of nutritional control of development and the contribution of epigenomic regulation to the evolution of sociality. I also argue that the term 'insect epigenetics' needs to be carefully redefined to reflect the diversity of epigenomic toolkits in insects and the impact of lineage-specific innovations on organismal outcomes. The honeybee genome project helped pioneer advances in social insect molecular biology, and fuelled breakthrough research into the role of flexible epigenomic control systems in linking genotype to phenotype.

Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.

APPLICATION OF THE RED PITAYA PLATFORM IN THE TECHNOLOGY OF PROTOTYPING AUTOMATED EDDY CURRENT TESTING SYSTEMS

Automated eddy current non-destructive testing has an important role in the quality inspection system for products made of conductive materials both at the production stage and during their operation. The appearance of novel alloys, the complexity of product geometry and inspection conditions, and the increased requirements for the reliability and accuracy of inspection results stimulate the acceleration process of creating new eddy current inspection tools. The technology of prototyping makes it possible to solve this problem. The purpose of the article is to review the functionality and technical characteristics of Red Pitaya for use in the technology of prototyping eddy current inspection, as well as to create a prototype architecture of an automated eddy current inspection system based on it.The choice of the Red Pitaya platform as a convenient tool for prototyping automated systems was made due to its functionality, reliability, and availability. In general, the article highlights the advantages and prospects of using the Red Pitaya platform in the prototyping of automated eddy current non-destructive testing systems. The aspects of the Red Pitaya hardware and its capabilities for expanding the functionality, which allows to create highly efficient and flexible control systems, are considered in detail. The compatibility of Red Pitaya with other devices and accessories is considered, which significantly increases its versatility and adaptability to various requirements and applications.The article highlights the importance of using the Jupyter Notebook application in combination with Red Pitaya as a convenient tool for software development. This helps to facilitate the process of creating and programmatically implementing complex signal processing algorithms in eddy current control systems, and provides flexibility in creating new software products.Modern trends and prospects for the development of eddy current testing, market needs, and the ability to adapt to different application scenarios are taken into account in the proposed architecture of the prototype automated eddy current testing system.

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception, force interaction, spatial obstacle avoidance, and motion planning. To ensure the reliability and determinism of system execution, a flexible real-time control system architecture and interaction algorithm are required. The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure, hardware abstraction, message-passing mechanism, and application prototypes. Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system. However, because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel, the system does not have real-time computing. To achieve a balance between real-time and non-real-time computing, this paper uses the transmission mechanism of ROS2, combines it with the scheduling mechanism of the Linux operating system, and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2. The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives, including throughput, transmission mode, QoS service quality, frequency, number of subscription nodes and EtherCAT master. This paper makes two significant contributions: firstly, it employs Preempt_RT to optimize the native ROS2 system, effectively enhancing the real-time performance of native ROS2 message transmission; secondly, it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems. This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance, with results vividly demonstrated through illustrative figures.

A High Robust Optimal Nonlinear Control with MPPT Speed for Wind Energy Conversion System (WECS) Based on Doubly Fed Induction Generator (DFIG)

As part of efforts to improve wind energy production using a doubly fed induction generator (DFIG), this paper presents modeling with power maximization through control of speed (MPPT) and control of stator active and reactive power for doubly fed induction generator. The quality of the generated energy and the performance of wind energy conversion systems (WECS) based on DFIG are affected by uncertainties and external disturbances. Therefore, the system requires high-performance control under the nonlinearity influence and the applied external disturbances. This work provides a new contribution and approach that combines nonlinear control (Backstepping) and optimal control (LQR). Backstepping control has been applied to give a good performance using nonlinear strategy and stability condition, but its main drawback is that it is relatively robust for sudden changes in wind speed or any external disturbances as well as it is sensitive against the uncertainties, which may lead to a chattering and an instability as well as overshoot or undershoot. To overcome the problems mentioned above the optimal control (LQR) has been applied to address this drawback in nonlinear control. Furthermore, it, the hybrid approach of Backstepping and LQR control resulting a robust control under the hardest scenarios during the wind system operating as well as provides a good performance in terms of chattering, stability and other performance characteristics which help to produce a high-quality energy. Overall, integrating LQR control with Backstepping can lead to a more powerful, flexible, and efficient control system with improved performance and reduced computational complexity.

Test Method of Secure and Controllable FPGA Chip Used in Flexible DC Transmission Control and Protection System

Flexible DC transmission control and protection system is a high-performance distributed computing system, which has the characteristics of high-speed data acquisition and fast processing, and uses a large number of high-performance FPGA (Field Programmable Logic Array) chips. FPGA has the characteristics of high integration and complex manufacturing process, and its security and controllability is faced with many challenges. The reliability of FPGA is an important standard to measure its performance, so reliability test fault screening is very important to ensure its normal operation. Although the automatic test equipment (ATE) can test the fault of FPGA, it is not convenient for field testing due to the price, test environment and other factors. In this paper, a design scheme of built-in self-test (BIST) for fault detection and fault diagnosis is proposed, which can detect faults of look-up table (LUT) and interconnection resources (wires and programmable switches). At the same time, considering the effect of rising temperature on fault probability, a controlled self-heating element (SHE) is constructed by using the internal resources of FPGA to provide the required temperature for BIST testing. Finally, an integrated layout scheme of BIST and SHE is proposed. An experiment of self-heating of FPGA chip is completed by TSINGHUA PGL22G, which verifies the correctness and feasibility of the theory.

Novel Control System Strategy for the Catalytic Oxidation of VOCs with Heat Recovery

A theoretical study of the dynamic closed-loop behaviour of a reactor/feed-effluent heat exchanger (FEHE)/furnace system for the catalytic combustion of volatile organic compounds (VOCs) is presented. A 1D pseudohomogeneous plug-flow model is proposed to simulate the non-steady-state operation of the monolith reactor and the FEHE, while the furnace behaviour is described by means of a heterogeneous model of lumped parameters. Positive energy feedback is a source of instability that leads to strong thermal oscillations (limit cycles) and may cause damage to the equipment and sintering of the catalyst. The design of a robust and flexible control system and an efficient control strategy are, therefore, required to ensure safe and stable operation. The response of the system under three different control strategies to the most frequent disturbance variables—the feed flowrate (FV0) and feed concentration of VOCs (C0Et)—was evaluated. One of the control strategies consisted of a single-loop feedback system with servomechanism changes in the reactor inlet temperature (T0) that manipulated the bypass valve and, sequentially, the natural gas flowrate in the furnace (FNG). This approach made it possible to meet the control objective (reducing VOCs) without losing controllability and while minimizing the use of external fuel.

Precision aiming performance with the paretic upper limb is associated with center of pressure patterns in individuals with chronic stroke

BackgroundIndividuals with stroke commonly demonstrate upper-limb sensorimotor impairments. Upper-limb tasks occur against a background level of postural control and thus require a flexible postural control system to facilitate performance. Anterior precision aiming tasks, for example, benefit from lower medial-lateral (ML) center of pressure (COP) fluctuations (where increased fluctuations erode performance) relative to anterior-posterior (AP) fluctuations (where increased fluctuations do not strongly influence performance). After stroke, individuals may compensate for upper-limb impairments by increasing trunk movement which increases overall COP fluctuations and thus may make it more difficult to modulate COP in a task-sensitive manner. Research questionDo upper-limb task demands modulate COP movement patterns after stroke? MethodsIn this cross-sectional study, adults with chronic stroke (n = 23) and unilateral upper-limb impairments were immersed in a virtual environment displaying an anterior target. Participants aimed to maintain the position of a virtual laser pointer (via handheld controller) in the target with each hand. COP was concurrently recorded. Mixed effects models and correlations were used to detect differences in COP patterns between limbs and movement planes and evaluate associations between task performance and COP patterns, respectively. ResultsParticipants showed greater COP standard deviation and regularity in the AP compared to the ML direction. The magnitude of difference between AP and ML COP metrics was greater using the nonparetic limb. Task performance was moderately and positively associated with task-sensitive COP patterns (i.e., higher AP:ML ratios of COP metrics) using the paretic upper limb. Participants consistently demonstrated high levels of task performance and task-sensitive COP movement patterns using the nonparetic limb. SignificanceImpairments in postural control after stroke may be related to the upper limb used. It is important to recognize the role of directional COP variability and regularity in the context of a task goal after stroke.

An adaptive and flexible biomass power plant control system based on on-line fuel image analysis

An adaptive and flexible biomass power plant control system based on on-line fuel image analysis

When interlocutor's face-language matching alters: An ERP study on face contexts and bilingual language control in mixed-language picture naming.

The present study used event-related potentials (ERP) to examine Chinese-English bilinguals' reactive and proactive language control as they performed mixed-language picture naming with face cues. All participants named pictures in Chinese (first language, L1) and English (second language, L2) across three sessions: a 25% face-language matched session, a baseline session without face cues, and a 75% face-language matched session. Behavioral analyses for reactive language control showed that the asymmetrical switch cost was larger for L2 than L1 in the 25% session and for L1 than L2 in the 75% session. ERP results revealed more negative N2 and LPC during L1 switching in 25% session but enhanced N2 during L2 switching in 75% session. Similar N2 and LPC effect was found during L1 and L2 switching in the baseline context. For proactive language control, the reversed language dominance and enhanced LPC amplitudes during L2 naming were consistent across the three sessions. Our findings suggest that reactive but not proactive language control is modulated by the ever-changing face contexts, which highlights the highly flexible bilingual control systems subserving nonlinguistic cues.

ICMS: A Flexible Location-Based Access Control System for Mobile Devices

In this article, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I Control My Space</i> (ICMS), a novel, fine-grained, and flexible system that allows space owners to define and enforce access restriction and service policies on mobile devices at their premises. This is achieved by enforcing restrictions on the use of applications and even specific permissions on the mobile device (e.g., camera, microphone, etc.). The system is built around a centralized database, where space owners can securely define their restrictions or even delegate control of their space to another owner. ICMS is designed with abstraction in mind so that it can be customized on existing access technologies and platforms—such as Android-based mobile devices—for various levels of security and privacy. A proof-of-concept prototype of ICMS is developed under the Android operating system, and promising results of its performance and effectiveness are demonstrated.

Polyethylene Glycol/Polyurethane Acrylate-Based Flexible Phase-Change Film with Excellent Mechanical Strength and Reversible Optical Performance

The application of thermal energy storage (TES) technology in practical engineering has been limited by liquid leakage and material rigidity during phase change. In this study, we prepared novel polyethylene glycol (PEG)/polyurethane acrylate (PUA)-based flexible phase-change films (PCFs) using ultraviolet (UV) curing technology to solve these problems. The PCFs showed a desirable phase-change enthalpy (113.1 J/g), low leakage rate (1.70%), reversible optical performance, excellent shape stability, and superior flexibility. The tensile strength of the PCF was 5.00 MPa, and the elongation at break reached 1342.3%, which was five times higher than that of pure PUA, indicating outstanding flexibility and toughness. The as-prepared PCFs show great potential for future applications, such as incorporation into flexible electronic devices and temperature control systems.