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

Zhijie Wang, Yuheng Huang, Da Song et al.

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model’s interpretation and the user’s intent thus becomes crucial. However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts. To address these challenges, we propose PromptCharm, a mixed-initiative system that facilitates text-to-image creation through multi-modal prompt engineering and refinement. To assist novice users in prompting, PromptCharm first automatically refines and optimizes the user’s initial prompt. Furthermore, PromptCharm supports the user in exploring and selecting different image styles within a large database. To assist users in effectively refining their prompts and images, PromptCharm renders model explanations by visualizing the model’s attention values. If the user notices any unsatisfactory areas in the generated images, they can further refine the images through model attention adjustment or image inpainting within the rich feedback loop of PromptCharm. To evaluate the effectiveness and usability of PromptCharm, we conducted a controlled user study with 12 participants and an exploratory user study with another 12 participants. These two studies show that participants using PromptCharm were able to create images with higher quality and better aligned with the user’s expectations compared with using two variants of PromptCharm that lacked interaction or visualization support.

Chongyoung Chung, Heeju Mun, Seyed Farokh Atashzar et al.

This article presents a novel, versatile wearable force myography (FMG) system based on optical fiber technology, designed for high sensitivity and mechanical robustness. Unlike conventional FMG systems, which are susceptible to environmental interference, the proposed system utilizes light loss through controlled fiber–polymer contact to achieve stable and noise‐free signal transmission. Its compact and flexible form factor allows seamless integration into wearable devices, facilitating muscle‐activity monitoring under diverse real‐world conditions, including biologically challenging scenarios such as sweating. Experimental evaluations highlight the system's ability to detect even micronewton‐scale forces and accurately recognize multiple gestures. Furthermore, the system can estimate joint angles, including those of individual fingers, which underscores its potential for precise motion capturing and continuous tracking. Overall, the proposed FMG system represents a promising solution for a wide range of practical human–robot interaction applications.

Shaobo Shen, Haochen Zhang, Tao Li et al.

The event-triggered predictor design problem for a class of nonlinear MIMO systems with large time delays is investigated in this paper. A periodic event-triggered mechanism is designed to avoid unnecessary data transmission and save communication energy. The triggering condition is only determined by sampled outputs of the system, such that it is applicable in case of the communication delay. Then a novel observer-based cascade predictor is proposed to reconstruct the original system state based on the delayed and event-triggered measurements, which is composed of a continuous-discrete observer and several sub-predictors in a chain structure. The stability of proposed predictor is analyzed through the Lyapunov approach. By using a sufficient number of sub-predictors and applying appropriate parameters, the prediction errors converge to bounded regions exponentially under large time delays. Finally, simulations are performed to verify the effectiveness of the proposed predictor and the event-triggered communication mechanism.

Yanchang LV, Jingyue Wang, Chengqiang Zhang et al.

For the strong noise gear fault vibration signal is relatively weak, and the transmission path is complex and variable, in the case of composite faults, the modulation of different fault characteristics of the frequency, coupling, resulting in the actual acquisition of the fault characteristics are difficult to extract and separate. Aiming at fault feature extraction and separation, an adaptive threshold denoising fault detection method based on Maximum correlated kurtosis deconvolution (MCKD) and Empirical wavelet transform (EWT) is proposed. Firstly, envelope entropy and information entropy are used as fitness functions, and the parameters of the MCKD algorithm are optimized by the improved particle swarm algorithm, then the empirical wavelet decomposition is carried out on the signals, and finally adaptive wavelet threshold denoising is carried out on the decomposed Intrinsic mode functions (IMFs) components. The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.

Mouhamad Haidar Lakkis, Jennifer Watchi, Rasa Jamshidi et al.

This paper addresses a theoretical control approach and its corresponding experimental validation for low-frequency active damping and isolation of a six-degree-of-freedom platform using high-resolution inertial sensors. Six vacuum-operating inertial sensors are placed on top of the platform to actively control it. Three of them measure displacements in horizontal directions and three in vertical directions. The resonance frequencies of the vertical and horizontal sensors range between 0.3 and 0.7 Hz with a resolution of 2 × 1 0 − 13 m / Hz at 1 Hz for both types of sensors. Sensor signals are fed back into six voice coil actuators (three horizontal and three vertical) mounted below the platform. Actuators and sensors are placed in a quasi-collocated architecture facilitating the controllability of the plant. The platform (with resonance frequencies ranging between 1 and 10 Hz) is actively isolated by up to two orders of magnitude between 0.1 and 10 Hz, yielding a final overall displacement RMS value below 100 nm at its center of mass from 0.3 Hz onward.

Robin Strässer, Karl Worthmann, Igor Mezić et al.

Controlling nonlinear dynamical systems remains a central challenge in a wide range of applications, particularly when accurate first-principle models are unavailable. Data-driven approaches offer a promising alternative by designing controllers directly from observed trajectories. A wide range of data-driven methods relies on the Koopman-operator framework that enables linear representations of nonlinear dynamics via lifting into higher-dimensional observable spaces. Finite-dimensional approximations, such as extended dynamic mode decomposition (EDMD) and its controlled variants, make prediction and feedback control tractable but introduce approximation errors that must be accounted for to provide rigorous closed-loop guarantees. This survey provides a systematic overview of Koopman-based control, emphasizing the connection between data-driven surrogate models, approximation errors, controller design, and closed-loop guarantees. We review theoretical foundations, error bounds, and both linear and bilinear EDMD-based control schemes, highlighting robust strategies that ensure stability and performance. Finally, we discuss open challenges and future directions at the interface of operator theory, approximation theory, and nonlinear control.

Michael Hertneck, David Meister, Frank Allgöwer

The defining characteristic of event-based control is that feedback loops are only closed when indicated by a triggering condition that takes recent information about the system into account. This stands in contrast to periodic control where the feedback loop is closed periodically. Benefits of event-based control arise when sampling comes at a cost, which occurs, e.g., for Networked Control Systems or in other setups with resource constraints. A rapidly growing number of publications deals with event-based control. Nevertheless, some fundamental questions about event-based control are still unsolved. In this article, we provide an overview of current research trends in event-based control. We focus on results that aim for a better understanding of effects that occur in feedback loops with event-based control. Based on this summary, we identify important open directions for future research.

W. Larimore

Shuhui Guo, Juhua Du, Donghan Li et al.

Inducible transcription systems are essential tools in genetic engineering, where tight control, strong inducibility and fast response with cost-effective inducers are highly desired. However, existing systems in yeasts are rarely used in large-scale fermentations due to either cost-prohibitive inducers or incompatible performance. Here, we developed powerful xylose and arabinose induction systems in Saccharomyces cerevisiae, utilizing eukaryotic activators XlnR and AraRA from Aspergillus species and bacterial repressors XylR and AraRR. By integrating these signals into a highly-structured synthetic promoter, we created dual-mode systems with strong outputs and minimal leakiness. These systems demonstrated over 4000- and 300-fold regulation with strong activation and rapid response. The dual-mode xylose system was fully activated by xylose-rich agricultural residues like corncob hydrolysate, outperforming existing systems in terms of leakiness, inducibility, dynamic range, induction rate, and growth impact on host. We validated their utility in metabolic engineering with high-titer linalool production and demonstrated the transferability of the XlnR-based xylose induction system to Pichia pastoris, Candida glabrata and Candida albicans. This work provides robust genetic switches for yeasts and a general strategy for integrating activation-repression signals into synthetic promoters to achieve optimal performance.

Nusrat Jahan, Tawab Bin Maleque Niloy, Jannatul Fahima Silvi et al.

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO 2 , O 3 , SO 2 , and NO 2 in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments.

Sun Qi, Zhang Kaiyin

Resonance of multi-degree-of-freedom system or structure is a basic and important concept in structural vibration theory, but it lacks a complete and rigorous definition. In order to establish an accurate concept of structural resonance, based on the concept of single-degree-of-freedom system resonance and modal orthogonality, this paper discusses the necessary conditions of system resonance by analyzing the displacement response of multi-degree-of-freedom vibration system, that is, while ensuring that the vibration frequency of the system (a certain natural frequency) is equal to the excitation frequency, its displacement response should also present the corresponding modal shape. An example of simply supported beam is used to illustrate its rationality. At the same time, the theoretical method of pure modal resonance of multi-degree-of-freedom system is given by rational allocation of excitation force. The pure modal resonance of multi-degree-of-freedom system or structure can be realized, which can be used to accurately identify the modal parameters of the structure. It is of great theoretical significance and engineering application value to discriminate the concept of multi-degree-of-freedom system or structure resonance.

Julian Berberich, Frank Allgöwer

The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closed-loop system is of crucial importance to ensure reliable operation. In this review, we provide an overview of data-driven model predictive control (MPC) methods for controlling unknown systems with guarantees on systems-theoretic properties such as stability, robustness, and constraint satisfaction. The considered approaches rely on the Fundamental Lemma from behavioral theory in order to predict input-output trajectories directly from data. We cover various setups, ranging from linear systems and noise-free data to more realistic formulations with noise and nonlinearities, and we provide an overview of different techniques to ensure guarantees for the closed-loop system. Moreover, we discuss avenues for future research that may further improve the theoretical understanding and practical applicability of data-driven MPC.

Mostafaali Ayubirad, Hamid R. Ossareh

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this paper proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell systems through simulation-based case studies.

P. Hanke, B. Parrello, O. Vasieva et al.

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4–5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

Yunyang Zhang, Zhiqiang Gong, Xiaoyu Zhao et al.

Recovering the global accurate complex physics field from limited sensors is critical to the measurement and control of the engineering system. General reconstruction methods for recovering the field, especially the deep learning with more parameters and better representational ability, usually require large amounts of labeled data which is unaffordable in practice. To solve the problem, this paper proposes uncertainty guided ensemble self-training (UGE-ST), using plentiful unlabeled data to improve reconstruction performance and reduce the required labeled data. A novel self-training framework with the ensemble teacher and pre-training student designed to improve the accuracy of the pseudo-label and remedy the impact of noise is first proposed. On the other hand, uncertainty guided learning is proposed to encourage the model to focus on the highly confident regions of pseudo-labels and mitigate the effects of wrong pseudo-labeling in self-training, improving the performance of the reconstruction model. Experiments including the airfoil velocity and pressure field reconstruction and the electronic components’ temperature field reconstruction indicate that our UGE-ST can save up to 90% of the data with the same accuracy as supervised learning.

Chen-Xu Deng, Xueqin Lv, Jianghua Li et al.

Wang Jianghua

YU Hui, LU Shengwen, CHEN Yan et al.

In the on-board environment of new energy passenger vehicles, IGBT modules are prone to the risk of vibration fatigue failure due to the influence of road roughness and engine rotation. In order to ensure the reliability of IGBT modules in service, it is of great significance to evaluate their vibration fatigue lifetime. The internal structure of IGBT module is complicated, so it is impossible to directly evaluate the vibration fatigue lifetime based on a physical model alone. To this end, an evaluation method is proposed in this paper to determine the vibration fatigue lifetime of IGBT modules. Firstly, the failure lifetime data of IGBT modules under high vibration magnitudes are obtained by an acceleration test. Secondly, the reliability lifetime distribution under the actual working conditions is obtained from the lifetime distribution statistical model based on the Weibull distribution, and then the reliability lifetime data in the practical application is obtained. The experimental results show that this method can effectively predict the failure rate of IGBT modules used in batches, which provides guarantee for reasonable management and control of application risks.

Pol Mestres, Jorge Cortés

This paper proposes an optimization with penalty-based feedback design framework for safe stabilization of control affine systems. Our starting point is the availability of a control Lyapunov function (CLF) and a control barrier function (CBF) defining affine-in-the-input inequalities that certify, respectively, the stability and safety objectives for the dynamics. Leveraging ideas from penalty methods for constrained optimization, the proposed design framework imposes one of the inequalities as a hard constraint and the other one as a soft constraint. We study the properties of the closed-loop system under the resulting feedback controller and identify conditions on the penalty parameter to eliminate undesired equilibria that might arise. Going beyond the local stability guarantees available in the literature, we are able to provide an inner approximation of the region of attraction of the equilibrium, and identify conditions under which the whole safe set belongs to it. Simulations illustrate our results.

Anastasios Tsiamis, Ingvar Ziemann, Nikolai Matni et al.

This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been substantial progress across all areas of control, the theory is most well-developed when it comes to linear system identification and learning for the linear quadratic regulator, which are the focus of this manuscript. From a theoretical perspective, much of the labor underlying these advances has been in adapting tools from modern high-dimensional statistics and learning theory. While highly relevant to control theorists interested in integrating tools from machine learning, the foundational material has not always been easily accessible. To remedy this, we provide a self-contained presentation of the relevant material, outlining all the key ideas and the technical machinery that underpin recent results. We also present a number of open problems and future directions.