Hasil untuk "Control engineering systems. Automatic machinery (General)"

Chuen-Chien Lee

BI Zhongtian, ZHANG Xinrui, YUAN Xiwen et al.

To address a research gap in China on motion control of vehicle-mounted pantographs for electrified highways, this paper presents an in-depth study in this field and proposes a pantograph lifting/lowering control strategy and a lateral follow-up control strategy. The paper first describes the pantograph motion control system solution and then presents a kinematics analysis that identifies various pantograph motion states. Subsequent design based on analysis results establishes the two strategies. The effectiveness and feasibility of the devised system were verified through simulations and bench experiments. Results show that this system enables the functions of pantograph lifting/lowering and emergency pantograph lowering in case of clearance intrusion, with lateral follow-up deviations controlled within 0.3 m, demonstrating its good control performance. The research results of this paper provide reference for further research in related fields, and support technological development and industrial applications on China's electrified highways.

R. M. D. Souza, E. G. S. Nascimento, U. A. Miranda et al.

Abstract Application of deep-learning techniques has been increasing, which redefines state-of-the-art technology, especially in industrial applications such as fault diagnosis and classification. Therefore, implementing a system that can automatically detect faults at an early stage and recommend stopping of a machine to avoid unsafe conditions in the process and environment has become possible. This paper proposes the use of Predictive Maintenance model with Convolutional Neural Network (PdM-CNN), to classify automatically rotating equipment faults and advise when maintenance actions should be taken. This work uses data from only one vibration sensor installed on the motor-drive end bearing, which is the most common layout present in the industry. This work was developed under controlled ambient varying rotational speeds, load levels and severities, in order to verify whether it is possible to build a model capable of classifying such faults in rotating machinery using only one set of vibration sensors. The results showed that the accuracies of the PdM-CNN model were of 99.58% and 97.3%, when applied to two different publicly available databases. This demonstrates the model’s ability to accurately detect and classify faults in industrial rotating machinery. With this model companies can improve the financial performance of their rotating machine monitoring through reducing sensor acquisition costs for fault identification and classification problems, easing their way towards the digital transformation required for the fourth industrial revolution.

Xingyu Wang, Dazhi Wang, Mingtian Du et al.

With the development of autonomous driving technology, some special vehicles should also be developed into autonomous driving vehicles. The robotic vehicles are widely used in engineering operations, and the robotic manipulator is a common tool mounted on robotic vehicles. The autonomous driving of robotic vehicles requires not only automatic movement, but also the automation of the robotic manipulator drive system. Therefore, this paper proposes an adaptive trajectory tracking control of manipulator based on sliding mode control (SMC), which can more accurately describe the joint space movement of the manipulator system under multi task coupling and external interference. Considering the multi-tasking situation, the influence of obstacle avoidance and base tilt on the trajectory of manipulator is investigated by using extreme learning machine (ELM), and the framework of machine learning is established. The output based on ELM-SMC is used as the bottom-layer control. Then an effective trajectory compensation method is proposed as the top-level control to avoid the error accumulation in the periodic repeated operation of the manipulator. Finally, the application effect of trajectory error tracking and compensation of manipulator in complex tasks of dynamic environment is verified by simulation and experiments, which lays a foundation for the stability control of manipulator in practical complex engineering applications. Note to Practitioners—The motivation of this paper is to solve the problem of poor trajectory tracking control accuracy of the manipulator caused by the change of the direction of the manipulator base. It is suitable for the high-precision position control of the manipulator assembled on the mobile robotic vehicle. Nowadays, the autonomous driving technology is developing rapidly, and the automation of traffic will be widely popularized in the future. Therefore, we should not only pay attention to the autonomous driving function of passenger vehicles, but also give consideration to the autonomous driving technology of robotic vehicles applied in engineering. It not only needs to complete the driving function, but also needs to ensure the reliable operation of the manipulator. In this paper, we mathematically deduce the control model of the manipulator. Then, we use SMC as the trajectory tracking control method of the manipulator, and use ELM to estimate the trajectory error caused by the end trajectory of the manipulator in the non-horizontal state of the base. Furthermore, we propose an optimization algorithm to optimize the compensation coefficient of cubic spline and give a reasonable compensation scheme. Through simulation and experiments, it is preliminarily verified that the schemes proposed in this paper are feasible, but they lack the application verification in the actual robotic vehicles, and the model fitting part of machine learning is completed with the help of the host computer, and the calculation ability of the controller of the actual manipulator may also be the limitation of completing this scheme.

Oladiran Kayode Olajiga, Emmanuel Chigozie Ani, Kehinde Andrew Olu-lawal et al.

Intelligent Monitoring Systems (IMS) have emerged as indispensable tools in modern manufacturing, offering real-time insights into production processes, equipment performance, and quality control. This review provides an overview of the current state and future prospects of IMS in manufacturing environments.  The current state of IMS in manufacturing involves the integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics. These systems collect vast amounts of data from sensors, machines, and production lines, enabling real-time monitoring and analysis of various parameters. By employing AI and ML algorithms, IMS can identify patterns, predict anomalies, and optimize production processes, leading to increased efficiency, reduced downtime, and improved product quality. IMS play a crucial role in predictive maintenance, where they can anticipate equipment failures before they occur, thereby minimizing unplanned downtime and maintenance costs. Moreover, IMS facilitate condition-based monitoring, allowing manufacturers to monitor the health and performance of machinery in real-time and schedule maintenance activities accordingly, optimizing resource allocation and prolonging equipment lifespan. Furthermore, IMS contribute to quality control by continuously monitoring production processes and detecting deviations from desired specifications. By leveraging AI-driven algorithms, IMS can automatically adjust process parameters to maintain product quality standards and minimize defects, thereby enhancing overall product reliability and customer satisfaction. Looking ahead, the future perspectives of IMS in manufacturing are promising, with advancements in areas such as edge computing, robotics, and augmented reality poised to revolutionize manufacturing operations further. Edge computing enables data processing and analysis to occur closer to the data source, reducing latency and enhancing real-time decision-making capabilities. Robotics integration with IMS facilitates autonomous manufacturing processes, while augmented reality technologies provide intuitive interfaces for operators to interact with IMS data in real-time.  IMS represent a transformative technology in manufacturing, offering unprecedented levels of visibility, control, and optimization. As technology continues to evolve, IMS are poised to play an increasingly vital role in shaping the future of manufacturing, driving efficiency, productivity, and innovation.. Keywords: Monitoring, System, Intelligent, Manufacturing, Review, Perspectives.

Marvin Norda, Chris Engel, J. Rennies et al.

Cars, mobile phones, and smart home devices already provide automatic speech recognition (ASR) by default. However, human machine interfaces (HMI) in industrial settings, as opposed to consumer settings, operate under different conditions and thus, present different design challenges. Voice control, arguably the most natural form of communication, has the potential to shorten complex command sequences and menu structures in order to directly execute a final command. Therefore, this contribution explored how differing HMI scenarios could possibly be optimized, by either replacing or complementing existing touch control interactions with voice control. Typical commands from CNC milling machines and industrial robots were categorized by their complexity, quantified by menu level and the necessary number of interactions. The collected interaction data showed that voice control can already provide a time efficiency advantage at either one additional menu level or three touchscreen interactions. For complex commands, such as those needing five menu levels and seven interactions on the touchscreen, the time efficiency advantage of voice control can reach up to 67 %. Furthermore, the study shows the possibility of reducing machine operator training times when using voice control by significantly lower interaction times for the first repetition of the participants. Note to Practitioners—Several publications investigate the ergonomics, usability, and cognitive load of classic mouse and keyboard control, button control, touch control, gesture control, gaze control, or voice control in specific interaction scenarios. All publications state that these factors need to be considered for the development of modern human machine interfaces (HMI). Due by the complexity of these factors, it is difficult to develop general guidelines to build efficient HMIs independent from the machine or process. A lack of efficiency guidelines potentially hampers the development of new HMIs, currently necessary to address the new challenges in the digital production hall like increasingly complex machines, processes that become more individual as well as multiple machine operation. In order to inform HMI development, voice and touch control alternatives were empirically measured. Based on the collected data complexity time equivalents for each menu level and number of interactions were calculated. These time equivalents provide the opportunity for machine and programmable logic controller (PLC) manufacturers to evaluate their production processes and the related interaction processes regarding the potential efficiency benefits of voice control as a complement or substitute for the conventional HMI system. Using this model, the efficiency advantage of voice control can be estimated without implementing and testing a voice control on a real production machine. Thus, the potential benefit of implementing voice control can be assessed directly, avoiding expensive test runs.

Sunday Adeola Ajagbe, Oyetunde Adeoye Adeaga, Oluwaseyi Omotayo Alabi et al.

The home automation system described in this paper is low-cost, dependable, and versatile. It uses an Arduino microcontroller and Bluetooth internet protocol (IP) connectivity to allow authorized users to remotely access and control devices. The suggested system employs the internet of things (IoT), which is server-independent, to manage human-desired appliances ranging from industrial machinery to consumer products. In this project, we have taken a Bluetooth module that is programmed through an Arduino Nano to control various devices auto-switching of mechanical devices and monitoring of water level within a range of 130 m using an Android application. This is done to show the effectiveness and viability of this system. Each bulb was switched on/off remotely using a mobile phone successfully. The operation of the water pump attached to the source bucket were controlled from the phone while in manual mode and controlled by an ultrasonic sensor while in automatic mode. It enables remote control of a number of devices, including lights and pumps, and decision-making based on sensor feedback.

Zhihan Chen, Siyuan Huang, Yuebing Zheng

The local force field generated by light endows optical microrobots with remarkable flexibility and adaptivity, promising significant advancements in precise medicine and cell transport. Nevertheless, the automated navigation of multiple optical microrobots in intricate, dynamic environments over extended distances remains a challenge. Herein, a versatile control strategy aimed at navigating optical microrobotic swarms to distant targets under obstacles of varying sizes, shapes, and velocities is introduced. By confining all microrobots within a manipulation domain, swarm integrity is ensured while mitigating the effects of Brownian motion. Obstacle's elliptical approximation is developed to facilitate efficient obstacle avoidance for microrobotic swarms. Additionally, several supplementary functions are integrated to enhance swarm robustness and intelligence, addressing uncertainties such as swarm collapse, particle immobilization, and anomalous laser–obstacle interactions in real microscopic environments. We further demonstrate the efficacy and versatility of our proposed strategy by achieving autonomous long‐distance navigation to a series of targets. This strategy is compatible with both optical trapping‐ and nudging‐based microrobotic swarms, representing a significant advancement in enabling optical microrobots to undertake complex tasks such as drug delivery and nanosurgery and understanding collective motions.

SHEN Ziyang, BAI Jinlei, ZHONG Puhua et al.

At present, the locomotive automatic driving device has been applied to multiple lines and has achieved certain phased results. Due to factors such as complex application scenarios, strong non-linearity and discreteness of control objects, some problems and challenges have arisen during the operation assessment, including impulsive and surging phenomena during train interval operation, non-compliance with station benchmark parking accuracy requirements, failure to start on steep slopes, and difficulty in evaluating air braking performance. These problems seriously affect the smoothness of operation and driving safety. The article studies the automatic driving control technology for key scenarios. Through in-depth analysis of operating procedures and historical operating data, optimization of train dynamics model parameters, establishment of accurate basic resistance models and air braking models, design of expert knowledge base for smooth operation strategy, intelligent parking control strategy, air braking terminal predictive control method, model and data-driven safety evaluation method, the problems encountered in on-site operation are ultimately solved. By the proposed strategies, it achieves the goal of 100% fully automatic control for the main line operation of 20 000 ton combined trains, providing a technical foundation for the normalized application of locomotive automatic driving.

Hao-Nan Pei, Wen-Jing Zhou, Ming Luo

The formed surface quality of metallic sealing ring of aero-engine affects the aircraft service performance directly. However, existing inspection methods, such as the final destructive inspection and the line laser scanner section profile measurement, only evaluate the formed quality from a 2-D view, that is, single or multiple radial formed section profiles. The lack of geometric information of 3-D surface is not conducive to the comprehensive monitoring of forming quality and process planning. Therefore, based on the line laser scanners, this paper mainly proposes a vibration errors compensation method based on self-feature registration. Aiming at the problem of rigid transformation of the measurement profile caused by random vibration during the rotary motion of metallic sealing ring, the feature of measurement profile in stationary scene (MPSS), that is, the medial axis, is used as the reference for the correct pose of measurement profile. The principle of finding the correct pose of measurement profile in rotary motion scene (MPRMS) is to minimize the distance between the medial axes. Next, based on the rotary motion information of metallic sealing ring and the geometric information of measurement system, a 3-D reconstruction matrix is built, so as to convert each measurement profile to the base coordinate system in turn, and finally a 3-D dynamic measurement method for the metallic sealing ring forming surface is built. The effectiveness of the proposed method is verified through simulation experiment and real measurement.

Sarika S, S. Anitha Janet Mary

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter's potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind's maximum power.

Xinze Xi, Min Wang, Chao Xing et al.

Abstract Aiming at the problem of sub‐synchronous oscillations induced by direct‐drive wind farms with series‐compensated lines, this paper proposes an unknown system dynamics estimation‐based PI controller to achieve sub‐synchronous oscillation suppression. According to the mathematical model of the direct‐drive wind farm grid‐connected system, the relationship between the direct‐drive wind turbine grid‐side converter current inner‐loop PI controller and the sub‐synchronous components is first established. Secondly, the uncertain sub‐synchronous current components and voltage components in the series‐compensated lines of the direct‐drive wind farm are taken as the total disturbance, and an unknown system dynamic estimator‐based PI controller is designed by introducing the first‐order low‐pass filter operation. Then, the stability and convergence of the closed‐loop system are proved by Lyapunov theory. Finally, the Prony method is used to analyse the current signal output by the direct‐drive wind turbine, and the inherent characteristics of the negative damping of the SSO induced by the series‐compensated line of the direct‐driven wind farm are revealed. A comparative numerical simulation is carried out to demonstrate that the sub‐synchronous oscillations of the direct‐drive wind farm with series‐compensated lines can be suppressed under different operating conditions.

Manuel Lanchares, W. Haddad

Dissipative dynamical systems provide fundamental connections between physics, dynamical systems theory, and control science and engineering. In the deterministic setting, dissipativity theory has been extensively developed in the literature to provide a general framework for the analysis and design of control systems using an input-state-output system description based on generalized system energy considerations that uses a state-space formalism to link engineering systems with memory to well known physical phenomena. Recently, several results have appeared in the literature extending dissipativity notions to the stochastic setting in order to develop an analogous theory for stochastic dynamical systems. However, unlike the deterministic theory, which can involve either an energy balance or a power balance state dissipation inequality for characterizing system dissipativity, in the stochastic case this equivalence is far more nuanced. In this paper, we develop a general theory for stochastic dissipativity and present general conditions on the system drift and diffusion functions as well as the system energy storage and supply rates to provide an equivalence between the sample path dependent energetic (i.e., supermartingale) and the power balance (i.e., algebraic) forms for characterizing stochastic dissipativity.

Jiejun Wang, Huizhong Zeng, Yiduo Xie et al.

Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic computing systems has attracted tremendous interest. However, existing analog memristors are still far from accurate tuning of multiple conductance states, which are crucial from the device‐level view. Herein, a reliable analog memristor based on ion‐slicing single‐crystalline LiNbO3 (LNO) thin film is demonstrated. The highly ordered LNO crystal structure provides a stable pathway of oxygen vacancy migration, which is contributed to a stable Mott variable‐range hopping process in trap sites. Excellent analog switching characteristics with high reliability and repeatability, including long retention/great endurance with small fluctuation (fluctuated within 0.22%), a large dynamic range of two orders of magnitude, hundreds of distinguishable conductance states with tunable linearity, and ultralow cyclic variances for multiple weight updating (down to 0.75%), are realized with the proposed memristor. As a result, a multilayer perceptron with a high recognition accuracy of 95.6% for Modified National Institute of Standards and Technology dataset is realized. The proposed analog memristive devices based on ion‐slicing single‐crystalline thin films offer a novel strategy for fabricating high‐performance memristors that combined linear tunability and long‐term repeatability, opening a novel avenue for neuromorphic computing application.

NIE Huoyong, HU Zhifeng, SHEN Wenqiang et al.

The stability of urban rail transit vehicle-ground integrated communication system is one of the biggest problems faced by metro operatings. In order to improve the security and reliability of communication, the wireless vehicle-ground communication technology of urban rail transit has been upgraded from WLAN to long-term evolution technology(LTE). On the basis of comprehensive analysis of the LTE technology of urban rail transit vehicles, a train access unit for the LTE-M wireless private network vehicle-ground communication equipment is proposed, according to the requirements specification of urban rail transit vehicle-ground integrated communication system. It adopts 1.8 GHz TD-LTE proprietary mobile network and VPN security access technology to establish a secure and reliable communication link at the vehicle-ground end. Through the communication beating test, the train access unit can communicate with the ground server through the core network of the private network, the communication rate is stable, the delay is less than 150 ms and no packet loss phenomenon appears. It can meet the data access service requirements of carrying CBTC control service, passenger information system, video surveillance system, etc.

G. Hu, Yipeng Pang, Chao Sun et al.

Game theory, which studies the cooperation and conflict among multiple rational decision makers, called players, can be utilized to analyze a large class of engineering systems (for example, wireless communication networks and smart grids). A game usually consists of three components: the players; the players’ actions; and their objective functions, which the players try to either maximize (in which case the objective function is known as a utility or payoff function) or minimize (in which case the objective function is referred to as a cost or loss function). In general, the players’ objective functions are dependent on other players’ actions, which lead to the coupling between the players’ actions in the decision-making process. This article is concerned with static games, where the order of the players’ decisions is not important (see “Summary”).

M. Kostomakhin, E. Pestryakov

Artificial intelligence is stated to be more and more widely used in agriculture, as well as for the diagnostics of the agricultural machinery condition. It was noted that in besides software, new computing devices are developed that enable processing and storing large amounts of data. (Research  purpose) To create a neural network-based software package for remote diagnostics of the limit state of machinery individual components and assemblies. (Materials and methods) Foreign studies within the problem area were analysed. It was found out that for data collection for artificial intelligence there exist STM32 and Arduino microcontroller-based devices, and the Nvidia CUDA (Compute Unified Device Architecture) hardware and software platform is used. For the software was developed in the C / C ++ programming language, and the MS SQL Server database were used as a repository. The general software is emphasized to be able to run on all major operating systems such as Windows, Mac OS, Linux. The role of neural network is argued to be important since it integrated all program blocks and provides its own analysis.  (Results and discussion) The information from the diagnostics devices is accumulated in a database. The neural network created on the basis of this database is constantly learning and simultaneously analyzing incoming data in real time, automatically issuing its recommendations. It was found that the neural network created by the employees of the Federal Scientific Agroengineering Center VIM has more functional options, for example, it is able to work directly with devices and conduct a more detailed technical analysis. (Conclusions) A neural network for equipment condition diagnostics was created, which increases the efficiency of decision-making in case of repair, and improves forecast and predictability. The criteria for equipment operation were proposed.

D. Volke, Laura Friis, Nicolas T. Wirth et al.

Genome engineering of non-conventional microorganisms calls for the development of dedicated synthetic biology tools. Pseudomonas putida is a Gram-negative, non-pathogenic soil bacterium widely used for metabolic engineering owing to its versatile metabolism and high levels of tolerance to different types of stress. Genome editing of P. putida largely relies on homologous recombination events, assisted by helper plasmid-based expression of genes encoding DNA modifying enzymes. Plasmid curing from selected isolates is the most tedious and time-consuming step of this procedure, and implementing commonly used methods to this end in P. putida (e.g. temperature-sensitive replicons) is often impractical. To tackle this issue, we have developed a toolbox for both target- and self-curing of plasmid DNA in Pseudomonas species. Our method enables plasmid-curing in a simple cultivation step by combining in vivo digestion of vectors by the I-SceI homing nuclease with synthetic control of plasmid replication, triggered by the addition of a cheap chemical inducer (3-methylbenzoate) to the medium. The system displays an efficiency of vector curing >90% and the screening of plasmid-free clones is greatly facilitated by the use of fluorescent markers that can be selected according to the application intended. Furthermore, quick genome engineering of P. putida using self-curing plasmids is demonstrated through genome reduction of the platform strain EM42 by eliminating all genes encoding β-lactamases, the catabolic ben gene cluster, and the pyoverdine synthesis machinery. Physiological characterization of the resulting streamlined strain, P. putida SEM10, revealed advantageous features that could be exploited for metabolic engineering.

B. A. D. Silva, Paulo Mol, O. Fonseca et al.

The Border Gateway Protocol (BGP) orchestrates Internet communications between and inside Autonomous Systems. BGP's flexibility allows operators to express complex policies and deploy advanced traffic engineering systems. A key mechanism to provide this flexibility is tagging route announcements with BGP communities, which have arbitrary, operator-defined semantics, to pass information or requests from router to router. Typical uses of BGP communities include attaching metadata to route announcements, such as where a route was learned or whether it was received from a customer, and controlling route propagation, for example to steer traffic to preferred paths or blackhole DDoS traffic. However, there is no standard for specifying the semantics nor a centralized repository that catalogs the meaning of BGP communities. The lack of standards and central repositories complicates the use of communities by the operator and research communities. In this paper, we present a set of techniques to infer the semantics of BGP communities from public BGP data. Our techniques infer communities related to the entities or locations traversed by a route by correlating communities with AS paths. We also propose a set of heuristics to filter incorrect inferences introduced by misbehaving networks, sharing of BGP communities among sibling autonomous systems, and inconsistent BGP dumps. We apply our techniques to billions of routing records from public BGP collectors and make available a public database with more than 15 thousand location communities. Our comparison with manually-built databases shows our techniques provide high precision (up to 93%), better coverage (up to 81% recall), and dynamic updates, complementing operators' and researchers' abilities to reason about BGP community semantics.

Radek Guras, Radek Strambersky, Miroslav Mahdal

The article deals with the issue of using the Two Degree of Freedom (2DOF) PID controller to control an integral system and investigates by the simulation and experimental measurement what influence it has on the course of the control process compared to standard PID controller. The controlled plant is represented by the DC electric motor with worm gear and its output shaft rotation angle. The article studies the effect of the added parameters of the 2DOF controller on the dynamics of the closed-loop control. The influence of these parameters is then evaluated using the quality of feedback control criteria ITAE. The paper studies how the overshoot of the controlled variable during the setpoint step is eliminated using 2DOF control theory. The overshoot is caused due to an aggressive tuning of the controller to eliminate the disturbance effect on the controlled variable of the integral plants with dead zones.