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

CHEN Yanping, GUO Weimin, WANG Xiong et al.

The packed bed thermal energy storage (PBTES) system is one of the important method to address the issues of fluctuation and intermittency in the development and utilization of renewable energy, and it can effectively reduce the impact on the power grid during the grid connection of renewable energy. However, the heat transfer characteristic during heat charging process and the optimization design methods need to be further investigated. In this study, a concentric dispersion model considering heat loss is established, and the phase change and heat transfer characteristics of the PBTES system under different mass flow rates are numerically analyzed. Meanwhile, a formula describing the average phase change travel velocity is proposed to quickly evaluate the phase change and heat transfer performances of the PBTES system. Based on the average phase change travel velocity, an optimal design method of the cascaded PBTES system is further proposed. The results show that the temperature difference caused by heat loss of the PBTES system decreases with the increase of mass flow rate, while the heat transfer temperature difference shows inapparent variation. During the charging process, the phase change front position inside the PBTES system changes approximately linearly and the average phase change travel velocity increases from 3.27×10^-5 m/s to 1.01×10^-4 m/s when the mass flow rate increases from 40 kg/h to 120 kg/h. The optimal filling ratio of phase change material capsule (PCM capsule) in each layer of the cascaded PBTES system is 35:23:42 based on the average phase change travel velocity. Compared with the uniform filling ratio of 1:1:1, the charging time of the cascaded PBTES system with optimal filling ratio is shortened by about 11.4%, and the average heat transfer power shows the increase of about 6.7%. The above results confirm that the phase change travel velocity can effectively characterize the phase change heat transfer characteristics of PBTES system, and the optimization method based on the phase change travel velocity can provide guidance for the design of PBTES system.

Teruki Kato, Koshi Oishi, Seigo Ito

Classical proportional--integral--derivative (PID) control is widely employed in industrial applications; however, achieving higher performance often motivates the adoption of model predictive control (MPC). Although gradient-based methods are the standard for real-time optimization, sampling-based approaches have recently gained attention. In particular, model predictive path integral (MPPI) control enables gradient-free optimization and accommodates non-differentiable models and objective functions. However, directly sampling control input sequences may yield discontinuous inputs and increase the optimization dimensionality in proportion to the prediction horizon. This study proposes MPPI--PID control, which applies MPPI to optimize PID gains at each control step, thereby replacing direct high-dimensional input-sequence optimization with low-dimensional gain-space optimization. This formulation enhances sample efficiency and yields smoother inputs via the PID structure. We also provide theoretical insights, including an information-theoretic interpretation that unifies MPPI and MPPI--PID, an analysis of the effect of optimization dimensionality on sample efficiency, and a characterization of input continuity induced by the PID structure. The proposed method is evaluated on the learning-based path following of a mini forklift using a residual-learning dynamics model that integrates a physical model with a neural network. System identification is performed with real driving data. Numerical path-following experiments demonstrate that MPPI--PID improves tracking performance compared with fixed-gain PID and achieves performance comparable to conventional MPPI while significantly reducing input increments. Furthermore, the proposed method maintains favorable performance even with substantially fewer samples, demonstrating its improved sample efficiency.

Mert Sehri, Zehui Hua, F. D. A. Boldt et al.

Deep learning has revolutionized many industries by enabling models to automatically learn complex patterns from raw data, reducing dependence on manual feature engineering. However, deep learning algorithms are sensitive to input data, and performance often deteriorates under nonstationary conditions and across dissimilar domains, especially when using time-domain data. Conventional single-channel or parallel multi-source data loading strategies either limit generalization or increase computational costs. This study introduces selective embedding, a novel data loading strategy, which alternates short segments of data from multiple sources within a single input channel. Drawing inspiration from cognitive psychology, selective embedding mimics human-like information processing to reduce model overfitting, enhance generalization, and improve computational efficiency. Validation is conducted using six time-domain datasets, demonstrating that the proposed method consistently achieves high classification accuracy across various deep learning architectures while significantly reducing training times. The approach proves particularly effective for complex systems with multiple data sources, offering a scalable and resource-efficient solution for real-world applications in healthcare, heavy machinery, marine, railway, and agriculture, where robustness and adaptability are critical.

Azadeh Mokari, Oleg Ryabchykov, Thomas Bocklitz

1D data, such as time series, and spectroscopy contain rich information but pose challenges for machine learning, due to limited large, labeled datasets and absence of specialized pretrained neural networks. Existing 1D analysis methods often rely on traditional chemometric approaches and rarely exploit the full potential of online data augmentation, novel architectures, and explainability methods common in image analysis. To address these gaps, a novel approach is proposed that transforms 1D signals into 2D spider plot visualizations, enabling utilization of pretrained deep learning models originally developed for image datasets. The approach also allows transformation of model interpretation maps back to the original variable space, making them more intuitive. The general applicability of this method is demonstrated across multiple data types: Raman spectra, mid‐infrared spectra, electrocardiograms, and mass spectrometry data (MALDI‐IMS). The method achieves competitive performance, reaching a balanced accuracy of 99% in Raman‐based oil identification tasks, surpassing principal component analysis combined with linear discriminant analysis (94%). Performance across datasets reflects variability due to data complexity, highlighting the method's versatility and potential across diverse signal types. This visualization‐based strategy presents an innovative solution to overcome dataset‐size and model‐related limitations while enhancing interpretability in complex 1D data analysis.

Ya Deng, Wei Zhu, Huannan Zheng

Compared with bounded delays, distributed infinite delays are more general in practical systems. Event-triggered control can effectively reduce energy consumption and communication costs. This paper addresses time-varying formation control of general linear multi-agent systems with distributed infinite delays in both of their inputs and outputs. An observer-based event-triggered formation control protocol considering distributed infinite delays is proposed, which is related to the combined observed information and some formation compensation signals at triggering time instants. By utilizing inequality techniques, the desired time-varying formation can be implemented while Zeno-behavior is excluded. Some numerical simulations are carried out for demonstrating the validity of theoretical results.

Azrul Azim Abdullah Hashim, Nor Maniha Abdul Ghani, Mohammad Osman Tokhi

This study develops and validates a force feedback control system for automotive pedals utilizing an optimized PID controller using the hybrid Spiral Sine-Cosine algorithm (SSCA). The primary objective is to enhance system performance by integrating SSCA-tuned PID control and comparing results from simulation and Hardware-in-the-Loop (HIL) testing. Auto Regressive with Exogenous inputs (NARX) models were used as the system identification method for nonlinear dynamic system to accurately represent actuator and pedal force relationships. Results demonstrated that the HIL setup significantly improved performance metrics compared to simulations: overshoot decreased, rise time improved, and settling time reduced for various force parameters. The study confirms that SSCA-tuned PID control can be effectively implemented in real-life applications, particularly in force control pedal vehicles, with potential benefits including reduced driver fatigue due to the repetitive actions of pressing and releasing the vehicle pedal. Future research will aim to enhance this approach by integrating vehicle speed control with advanced actuator and pedal force control systems. This integration will ensure smoother and more precise control over vehicle dynamics, improving overall responsiveness and efficiency. Moreover, a primary focus will be on optimizing low-speed driving scenarios, particularly in traffic congestion, where precise control is critical. By addressing challenges such as stop-and-go movement, vehicle jerks, and energy efficiency, this research seeks to enhance both driver comfort and safety in urban traffic conditions.

Alvaro Ayuso‐Martinez, Daniel Casanueva‐Morato, Juan P. Dominguez‐Morales et al.

In recent years, physical limitations in the integration of transistors in computers have forced the search for low‐computational‐power alternatives in hardware design. Although doubts may arise regarding the limit of the relationship between performance and power consumption in computers, these disappear when considering the brain, which is one of the most efficient computing systems. In this way, bioinspired applications try to benefit from the low‐power consumption present in the biological nervous system. Previous work has shown the feasibility of implementing spiking neural networks that operate in a Boolean manner on digital platforms, such as SpiNNaker, using basic logic gates and a spiking memory, which suggests the potential for constructing a low‐power consumption spiking computer. This work takes a first step in the implementation of a spiking central processing unit by developing an arithmetic logic unit, which is an essential block for instruction execution, demonstrating its correct operation on Dynap‐SE1. The results confirm the feasibility of using this Boolean approach on this platform, despite certain limitations in the number of inputs and operating frequencies of the blocks, and pave the way for the construction of a spiking computer.

Mohamed Yassine Arkhis, Denis Efimov

In this paper, first, it is shown that if a nonlinear time-varying system is contractive, then it is incrementally exponentially stable. Second, leveraging this result, under mild restrictions, an approach is proposed to design feedforward inputs for affine in control systems providing contraction/incremental exponential stability. Unlike standard stability notions, which have well-established control design techniques, this note can be considered among the first ones to provide such a tool for a kind of incremental stability. The theoretical findings are illustrated by examples.

Xinhao Yan, Guanzhong Zhou, Daniel E. Quevedo et al.

Networked systems are increasingly the target of cyberattacks that exploit vulnerabilities within digital communications, embedded hardware, and software. Arguably, the simplest class of attacks – and often the first type before launching destructive integrity attacks – are eavesdropping attacks, which aim to infer information by collecting system data and exploiting it for malicious purposes. A key technology of networked systems is state estimation, which leverages sensing and actuation data and first-principles models to enable trajectory planning, real-time monitoring, and control. However, state estimation can also be exploited by eavesdroppers to identify models and reconstruct states with the aim of, e.g., launching integrity (stealthy) attacks and inferring sensitive information. It is therefore crucial to protect disclosed system data to avoid an accurate state estimation by eavesdroppers. This survey presents a comprehensive review of the existing literature on privacy-preserving state estimation methods, while also identifying potential limitations and research gaps. Our primary focus revolves around three types of methods: cryptography, data perturbation, and transmission scheduling, with particular emphasis on Kalman-like filters. Within these categories, we delve into the concepts of homomorphic encryption and differential privacy, which have been extensively investigated in recent years in the context of privacy-preserving state estimation. Finally, we shed light on several technical and fundamental challenges surrounding current methods and propose potential directions for future research. Note to Practitioners—With the increasing openness and anonymization of the networked estimation systems, privacy concerns require to be paid more attention. The essence of the privacy-preserving approaches is to seek certain tradeoffs among privacy budget and various performance metrics, such as utility and energy. Cryptographic methods are suitable for high-performance processors because they need sufficient computation resources to generate and operate complicated secret keys. By contrast, perturbation methods can be realized faster, but the adverse impact on the legitimate systems should be limited not to violently disrupt the desired operations. In conclusion, the choice of these encryption approaches depends on practical demands. Moreover, general state-space models, which can represent most real-world dynamics, are the basis of the reviewed methods. Thus these approaches can be easily deployed to practical engineering systems to effectively guarantee their privacy, providing significant application values.

Ievgen Zaitsev, Andrii Shpylka, Zakusilo Sergy et al.

Jian Chang, Shuze Zhu

Strain and defect engineering have profound applications in two‐dimensional materials, where it is important to determine the equilibrated atomistic structures with defect conditions under mechanical deformations for computational materials design. Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge. To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defect‐engineered atomistic geometry, so that deformation‐coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone–Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain‐embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. The computational approach sheds light on boosting the efficiency of evaluating physical properties of 2D materials under complex strain and defect states.

Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio et al.

Herein, an innovative deep‐learning architecture is proposed to enhance the sensing capabilities of a microelectromechanical system (MEMS) used in fluid dynamic applications. The MEMS sensor comprises a polyvinylidene fluoride flexible (PVDF) piezoelectric flag and a bluff body, with vortex generation influenced not only by the bluff body's geometry but also by the input fluid speed. As a result, mechanical vibrations are induced in the piezoelectric flag, leading to charge displacement and the generation of electrical voltage signals. Through the developed deep learning method, accurate extraction of wind speed and successful classification of turbulence are achieved. Experimental tests in a wind tunnel, involving various wind speeds and bluff body geometries, demonstrate the robust correlation between the extracted continuous manifold in Fourier spectra and wind speed. By incorporating a feed‐forward network alongside the autoencoder, wind speed information even under strong turbulence is extracted. Moreover, the deep learning method's ability to classify different bluff bodies, independent of wind speed, is investigated. The findings reveal a unique capability to fingerprint turbulence and distinguish them for various applications. This research showcases the potential of our deep learning‐based MEMS systems for enhancing fluid dynamic sensing and classification tasks.

YU Juan, ZHOU Sheng, PENG Jing et al.

This paper explores the issue of axle deviation caused by the accuracy deterioration of steering angle sensors on autonomous-rail rapid trams (ART), focusing specifically on its effects on vehicle operating postures. Based on vehicle dynamics modeling, the study investigated the impact of axle deviation on vehicle riding performance, through lateral dynamics analysis and calculations. Multi-body system dynamics simulations were conducted to verify the reliability of the calculation results, further revealing the correlations between different axle deviation angles and changes in vehicle operating postures. Research results indicated that emerging axle deflection angles caused ARTs to operate in a skewed posture during straight running, and the steering angle and articulation angle of each axle were found to be linearly correlated with the axle deflection angles. In addition, compared to single-axle deflection angles, the coupling of multi-axle deflection angles displayed a superposition characteristic in vehicle appearance features, which facilitates the positioning analysis of faults in steering angle sensors. This paper also proposes suggestions on the diagnosis method of faults related to accuracy degradation suffered by steering angle sensors on ARTs, along with effective measures to improve the safety and reliability of ART operation.

Xiaoyan Hu, Prathyush P Menon, Christopher Edwards et al.

Abstract Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.

Luca Ballotta, Geethu Joseph, Irawati Rahul Thete

This paper considers the design of sparse actuator schedules for linear time-invariant systems. An actuator schedule selects, for each time instant, which control inputs act on the system in that instant. We address the optimal scheduling of control inputs under a hard constraint on the number of inputs that can be used at each time. For a sparsely controllable system, we characterize sparse actuator schedules that make the system controllable, and then devise a greedy selection algorithm that guarantees controllability while heuristically providing low control effort. We further show how to enhance our greedy algorithm via Markov chain Monte Carlo-based randomized optimization

Risvan Dirza, Hari Prasad Varadarajan, Vegard Aas et al.

This paper considers the problem of steady-state real-time optimization (RTO) of interconnected systems with a common constraint that couples several units, for example, a shared resource. Such problems are often studied under the context of distributed optimization, where decisions are made locally in each subsystem, and are coordinated to optimize the overall performance. Here, we use distributed feedback-optimizing control framework, where the local systems and the coordinator problems are converted into feedback control problems. This is a powerful scheme that allows us to design feedback control loops, and estimate parameters locally, as well as provide local fast response, allowing different closed-loop time constants for each local subsystem. This paper provides a comparative study of different distributed feedback optimizing control architectures using two case studies. The first case study considers the problem of demand response in a residential energy hub powered by a common renewable energy source, and compares the different feedback optimizing control approaches using simulations. The second case study experimentally validates and compares the different approaches using a lab-scale experimental rig that emulates a subsea oil production network, where the common resource is the gas lift that must be optimally allocated among the wells. %The pros and cons of the different approaches are discussed.

Audrey Blizard, Stephanie Stockar

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an average normalized root mean square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.

Victor Gracia, Pablo Krupa, Teodoro Alamo et al.

Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in embedded systems. To reduce the computational burden of the optimization algorithm, most solvers perform as many offline operations as possible, typically performing the computation and factorization of its expensive matrices offline and then storing them in the embedded system. This improves the efficiency of the solver, with the disadvantage that online changes on some of the ingredients of the MPC formulation require performing these expensive computations online. This article presents an efficient algorithm for the factorization of the key matrix used in several first-order optimization methods applied to linear MPC formulations, allowing its prediction model and cost function matrices to be updated online at the expense of a small computational cost. We show results comparing the proposed approach with other solvers from the literature applied to a linear time-varying system.

Bharati Ramteke, Snehlata Dongre

According to research there are an estimated 850 million poultry birds in India and the number of farmers involved in the business is estimated at 30 million. This clearly means that the poultry farm is one of the most important and healthy sources of income in India. But it does require a lot of effort to run a poultry farm, as it requires general bird control, health monitoring, Food, dihydrogen monoxide, and local hygiene. The actual process of all of this is more challenging and more difficult. Therefore in order to acquire the owners of the poultry farm. Proposed Intelligent Automated Poultry Management System using IoT. The program includes feeding and watering birds using sensors used inside containers. The system will set an alarm when the stock element and dihydrogen monoxide are down. The lights in the Poultry farm is control by a sensor. Automatic food distribution, pure supply of dihydrogen monoxide, egg accumulation can be done through this system.

Y. Tsench

The priority direction for the scientific and technological development of the agro-industrial complex is shown to be the transition to advanced digital smart technologies, robotic systems, new materials and design methods, to artificial intelligence. The importance of further evolution is noted for such areas as agro-engineering science and education, cooperation of agro-engineering institutions, innovative approaches and solutions that reflect the current state and tendencies in the development of the agro-industrial complex. The relevance is substantiated for studying the experience of the foundation and development of a system of agro-engineering scientific institutes, agro-engineering universities, machine trial stations, whose coordinated activity made it possible to form a powerful scientific and technological potential, build a multi-profile integrated agro-industrial production and ensure the country's food security.Research purpose To identify the general evolutionary factors in the development of specialized infrastructure institutions, the key development features of a scientific agro-engineering platform for creating the domestic systems and complexes of agricultural machines.Materials and methods Established the chronological framework of the study, noting the features of the three main periods of the last century. Characterized the reforms of agroengineering scientific and educational institutions.Results and discussion The evolutionary factors of the formation of agroengineering universities, scientific institutions, machine testing stations in 1920-2020 are investigated and identified.Conclusions It is proved that the formed and continuously developing scientific and technological potential became the basis for creating the systems of highly efficient agricultural machinery and equipment, contributed to the agriculture transformation into a highly efficient mechanized production and ensured the country's food security.