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

Alexander Korolev, Andrew Senkevich

Jingjing Sun

Ensieh Khorramian, Mohsen Rostamy-Malkhalifeh, Ahmad Shahvarani

Mathematics is one of the important subjects of a student at various stages. From the past to present, hearing the name of mathematics has been accompanied by fear and anxiety for a significant group of students. Quality of mathematics education is a complex and multifaceted concept, and various authors with different viewpoints have proposed methods regarding the quality. Decision making in the selection of the teaching method is one of the pillars of a teacher and applying proper methods which are appropriate to the educational environment has an effective role in the success of education and reduction of students’ anxiety. In this research, with the help of Network Data Envelopment Analysis (NDEA) and Sexton’s method, we are going to rank several different teaching methods to reduce the math anxiety of second year elementary school students. Several methods were implemented in the classrooms and after that the results were evaluated using the above method and it was found that the game method has the highest rank and based on this research, it will be tried to implement this method in the classroom. Lessons should be used to reduce students’ math anxiety. In this article, we delve into the study of practical scenarios and conditions that may make the utilization of these methods more effective in mathematics education. Additionally, we offer recommendations to teachers and individuals involved in math instruction to maximize the benefits of mathematical education and reduce math anxiety.

Tahiyatul Asfihani, Ahmad Maulana Syafi'i, Agus Hasan

Unmanned surface vehicles (USVs) require robust control systems capable of adeptly compensating for potential faults to ensure operational safety and successful task execution. Addressing this requirement, we present a novel approach for computing control inputs of USVs under fault-prone conditions. Our method leverages a mathematical model, specifically a linear stochastic discrete-time model that characterizes the USV subject to actuator faults. Central to our approach is the integration of an adaptive Kalman filter (AKF) with a forgetting factor into model predictive control (MPC). This fusion enables our proposed method to effectively manage actuator faults on the USVs. The essence of our fault-tolerant control strategy lies in utilizing the AKF within the MPC framework to predict both the stochastic system model and the actuator fault parameters. Through rigorous evaluation, we demonstrate the effectiveness of our proposed method in managing actuator faults on USVs. The results highlight its capacity to ensure operational continuity and task completion even in the presence of faults, demonstrating its significance for enhancing the resilience of USV control systems in real-world scenarios.

Jaesung Lee, Shiyu Zhou, Junhong Chen

In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. The performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners—Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.

N. Subha Lakshmi, S. Allirani

The permanent magnet synchronous motor (PMSM) avoids commutation-related torque ripples and produces smooth torque. Its great handling capacity and better efficiency make it an excellent choice for high-demand applications. A typical PM motor drive fed with pulse width-modulated voltages may cause the motor insulation to break down if rapid voltages (dv/dt) occur across the motor terminals. Applying variable voltage with low dv/dt and implementing multiple inverter topology can solve this issue. Multilevel converters have minimal switching losses, better power quality and the ability to operate at both fundamental and higher switching frequencies. In this study, a three phase stacked multilevel inverter-based FOC driven PMSM drive design is proposed. Here, the neutral point is a capacitor intermediate point on DC side, where current is naturally balanced throughout a switching cycle. This makes it possible to use downstream batteries and even lower voltage equipment, greatly increasing efficiency, improved performance and smother control at low speed. Therefore, direct-battery-driven electric vehicles will be able to use this. A 33 level inverter-based PMSM drive was used to implement the FOC, and the simulation results were used to validate it. The Matlab/Simulink tool is used to simulate the entire system.

N. Kuznetsov, M. Y. Lobachev, M. Yuldashev et al.

Phase-locked loops (PLLs) are nonlinear automatic control circuits widely used in telecommunications, computer architecture, gyroscopes, and other applications. One of the key problems of nonlinear analysis of PLL systems has been stated by Floyd M. Gardner as being “to define exactly any unique lock-in frequency.” The lock-in range concept describes the ability of PLLs to reacquire a locked state without cycle slipping and its calculation requires nonlinear analysis. This work analyzes a second-order type 2 PLL with a sinusoidal phase detector characteristic. Using the qualitative theory of dynamical systems and classical methods of control theory, we provide stability analysis and suggest analytical lower and upper estimates of the lock-in range based on the exact lock-in range formula for a second-order type 2 PLL with a triangular phase detector characteristic. Applying phase plane analysis, an asymptotic formula for the lock-in range, which refines the existing formula is obtained. The analytical formulas are compared with computer simulation and engineering estimates of the lock-in range. The comparison shows that engineering estimates can lead to cycle slipping in the corresponding PLL model and cannot provide a reliable solution for the Gardner problem, whereas the lower estimate presented in this article guarantees frequency reacquisition without cycle slipping for all parameters, which provides a solution to the Gardner problem.

G. Cavone, T. van den Boom, L. Blenkers et al.

Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling algorithm for real-time control of railway traffic that aims at minimizing the delays induced by the disruption and disturbances, as well as the resulting cancellations of train runs and turn-backs (or short-turns) and shuntings of trains in stations. The real-time control is based on the Model Predictive Control (MPC) scheme where the rescheduling problem is solved by mixed integer linear programming using macroscopic and mesoscopic models. The proposed resolution algorithm combines a distributed optimization method and bi-level heuristics to provide feasible control actions for the whole network in short computation time, without neglecting physical limitations nor operations at disrupted stations. A realistic simulation test is performed on the complete Dutch railway network. The results highlight the effectiveness of the method in properly minimizing the delays and rapidly providing feasible feedback control actions for the whole network. Note to Practitioners—This article aims at contributing to the enhancement of the core functionalities of Automatic Train Control (ATC) systems and, in particular, of the Automatic Train Supervision (ATS) module, which is included in ATC systems. In general, the ATS module allows to automate the train traffic supervision and consequently the rescheduling of the railway traffic in case of unexpected events. However, the implementation of an efficient rescheduling technique that automatically and rapidly provides the control actions necessary to restore the railway traffic operations to the nominal schedule is still an open issue. Most literature contributions fail in providing rescheduling methods that successfully determine high-quality solutions in less than one minute and include real-time information regarding the large-scale railway system state. This research proposes a semi-heuristic control algorithm based on MPC that, on the one hand, overcomes the limitations of manual rescheduling (i.e., suboptimal, stressful, and delayed decisions) and, on the other hand, offers the advantages of online and closed-loop control of railway traffic based on continuous monitoring of the traffic state to rapidly restore railway traffic operations to the nominal schedule. The semi-heuristic procedure permits to significantly reduce the computation time necessary to solve the rescheduling problem compared with an exact procedure; moreover, the use of a distributed optimization approach permits the application of the algorithm to large instances of the rescheduling problem, and the inclusion of both the traffic and rolling stock constraints related to the disrupted area. The method is tested on a realistic simulation environment, thus still requires further refinements for the integration into a real ATS system. Further developments will also consider the occurrence of various simultaneous disruptions in the network.

Mingyin Yang, Xiaonan Ren, Joung Hyung Cho

In this paper, a semi-active nonlinear artificial intelligence compound controller for marine engines was developed to improve vibration reduction characteristics across a wide frequency range. A mathematical model was developed and investigated for two-stage vibration isolation systems (Virgin IslandsS) considering vertical, roll, and pitch motion. The passive mathematical model of the magnetorheological damper was also developed and integrated with the two-stage VIS. The passive numerical analysis was validated through the experimental investigation. Force transmitted from the engine to the base was evaluated on the validated model using four different strategies, that is, conventional passive, semi-active low, semi-active high, and semi-active controlled damper. In a semi-active–controlled damper, a mathematical model is developed for controlling the force by developing a nonlinear artificial intelligent compound controller (NAICC) using the algorithm of chaotic fruit fly and fuzzy logic control. The results show that the application of NAICC has a better isolating effect than the passive VIS over a broad spectrum of frequencies. By strengthening the control effect in the low-frequency resonance zone, marine engine vibration reduction performance was significantly enhanced.

Haifeng Chen, Jinlei Zhuang, Bingyou Liu et al.

Camera calibration will directly affect the accuracy and stability of the whole measurement system. According to the characteristics of circular array calibration plate, a camera calibration method based on circular array calibration plate is proposed in this paper. Firstly, subpixel edge detection algorithm is used for image preprocessing. Then, according to cross ratio invariance and geometric constraints, the projection point position of the center point is obtained. Finally, the calibration experiment was carried out. Experimental results show that under any illumination conditions, the average reprojection error of the center coordinates obtained by the improved calibration algorithm is less than 0.12 pixels, which is better than the traditional camera calibration algorithm.

Dulyawat Doonyapisut, Padmanathan-Karthick Kannan, Byeongkyu Kim et al.

Technology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpreted due to the complexity of the electrochemical system or the compromise between the experimental result and the theoretical model, resulting in partiality in the interpretation process, especially for the impedimetric results. Typically, the experimenter interprets impedimetric results using a searching approach based on a theoretical model until the best‐fitting model is obtained, which is a time‐consuming process, and errors can occur. To reduce misinterpretation by the experimenter, herein, the machine‐learning strategy is demonstrated for the classification of an EIS circuit model and parameter prediction using a deep neural network (DNN). The DNN model shows a highly accurate classifier for the commonly used EIS circuit with an average area under the receiver operating characteristic curve of more than 0.95. Additionally, the model demonstrates high accuracy in the prediction of EIS parameters on a complex EIS system, with a maximum R2 of 0.999. These reveal that the machine‐learning strategy may open a new room for studying electrochemical systems.

Pallov Anand, A. Pedro Aguiar

Changpeng Li, Tianhao Peng, Yanmin Zhu et al.

In coal and rock recognition technology, the acquisition of sound signals is affected by background noise. It is challenging to extract cutting features and accurately identify cutting patterns effectively. Therefore, this paper proposes an approach for combined noise reduction of the cutting sound signal based on the improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN) and a singular value decomposition (SVD). First, the method used the ICEEMDAN method to decompose the noisy signal into several intrinsic mode functions (IMF). It calculated the correlation coefficient between the IMF component and the noisy signal and then selected the noisy IMF components based on the threshold formula. Meanwhile, this method constructed a Hankel matrix of the noisy IMF component signals. It used SVD technology to obtain the singular values. According to the singular value standard energy spectrum curve, the paper determined the order of the effective singular value and removed the noise component in the signal. Then, the denoised IMF and noiseless IMF components are superimposed and reconstructed to obtain the noise-reduced cutting sound signal. Finally, it applied simulation signal and simulated shearer cutting experiment to verify the performance of the method. The results show that the proposed method can effectively remove the influence of background noise in the signal and retain the characteristic frequencies of the original cutting sound signal. Compared with traditional noise reduction methods, the ICEEMDAN-SVD combined noise reduction method performs better in noise reduction evaluation standards of signal-noise ratio and root mean square error. It achieved a better noise reduction effect, which could help coal and rock recognition technology based on sound signals.

M. Jarke, B. Otto, S. Ram

Anqi Li, Ching-An Cheng, M. A. Rana et al.

We consider the problem of learning motion policies for acceleration-based robotics systems with a structured policy class. We leverage a multi-task control framework called RMPflow which has been successfully applied in many robotics problems. Using RMPflow as a structured policy class in learning has several benefits, such as sufficient expressiveness, the flexibility to inject different levels of prior knowledge as well as the ability to transfer policies between robots. However, implementing a system for end-to-end learning of RMPflow policies faces several computational challenges. In this work, we re-examine the RMPflow algorithm and propose a more practical alternative, called RMP2, that uses modern automatic differentiation tools (such as TensorFlow and PyTorch) to compute RMPflow policies. Our new design retains the strengths of RMPflow while bringing in advantages from automatic differentiation, including 1) simple programming interfaces to designing complex transformations; 2) support of general directed acyclic graph (DAG) transformation structures; 3) end-to-end differentiability for policy learning; 4) improved computational efficiency. Because of these features, RMP2 can be treated as a structured policy class for efficient robot learning that is suitable for encoding domain knowledge. Our experiments show that using the structured policy class given by RMP2 can improve policy performance and safety in reinforcement learning tasks for goal reaching in cluttered space. The video for our experimental results can be found at https://youtu.be/dliQ-jsYhgI and the code is available at https://github.com/UWRobotLearning/rmp2.

Hiroaki Kata, Seiya Ueno

Connectivity maintenance with application to target search considering the failure of unmanned vehicles is proposed. The unmanned vehicles form a network and exchange information with neighbours. Vehicle failures can cause network disconnection and disruption of information exchange. Therefore, the robust k-connected network, which the network is connected even if less than k unmanned vehicles fail, is configured in a decentralized system. Each vehicle determines the velocity input according to the partial vertex connectivity, which is an evaluation of connectivity for each vehicle, and triangulation input for collision avoidance. Target search simulation in the presence of obstacles shows that the proposed robust k-connected network control law is valid.

Qianying Chen, Pengyu Lv, Tian-Yun Huang et al.

Usually, it is indispensable for traditional functional robots to use flexible joints that integrate sophisticated machinery and control systems to achieve precise operability and efficient mobility. At the microscale, however, the conventional design of functional joints is generally not suitable due to the limitation of the manufacturing process on such a tiny size. Herein, a strategy for the design of smart microjoints (SMJs) that undergo controllable active deformation by triggering a size‐dependent layer‐by‐layer sequential swelling effect on SMJs in response to external stimuli is developed. The optimal encoding of SMJs that enables microcrawlers to achieve superior crawling speed (0.15 body length s−1) and efficiency (1.1 body length per step), as well as controllable locomotion, is demonstrated, e.g., migration along/against the stimuli source or along a preplanned path. A path toward constructing soft actuators/robots at the microscale with high adaptability and controllability for broad engineering applications is offered.

Bin Yang, Zhen-xing Liu, Hui-Kang Liu et al.

Zhisai Ma, Xiang Li, Meng-Xin He et al.

Hui Li, Ziheng Wang, Yongle Chang et al.

In this research, the characterization test on nonlinear vibration of a fibre-reinforced composite thin plate is studied. First, in order to improve the efficiency and precision of the test, a laser scanning vibration system is designed and developed. Then, test methods and procedures of the harmonic distortion and nonlinear time-varying damping of such thin plates are proposed from the time-domain perspective. Corresponding test methods and procedures of nonlinear vibration properties such as the hard/soft stiffness and amplitude-dependent damping are also presented from the frequency-domain perspective. Finally, the TC500 carbon fibre/resin composite plate is selected as an example for the research to carry out a case study. Potential nonlinear vibration phenomena of such plates are characterized by the proposed test methods. It has been proved that the related test system and techniques adopted in this paper can provide an important reference for the establishment of the nonlinear vibration test methodology of fibre-reinforced composite structures.