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

Aleš Gregor, E. Filová, M. Novák et al.

The primary objective of Tissue engineering is a regeneration or replacement of tissues or organs damaged by disease, injury, or congenital anomalies. At present, Tissue engineering repairs damaged tissues and organs with artificial supporting structures called scaffolds. These are used for attachment and subsequent growth of appropriate cells. During the cell growth gradual biodegradation of the scaffold occurs and the final product is a new tissue with the desired shape and properties. In recent years, research workplaces are focused on developing scaffold by bio-fabrication techniques to achieve fast, precise and cheap automatic manufacturing of these structures. Most promising techniques seem to be Rapid prototyping due to its high level of precision and controlling. However, this technique is still to solve various issues before it is easily used for scaffold fabrication. In this article we tested printing of clinically applicable scaffolds with use of commercially available devices and materials. Research presented in this article is in general focused on “scaffolding” on a field of bone tissue replacement. Commercially available 3D printer and Polylactic acid were used to create originally designed and possibly suitable scaffold structures for bone tissue engineering. We tested printing of scaffolds with different geometrical structures. Based on the osteosarcoma cells proliferation experiment and mechanical testing of designed scaffold samples, it will be stated that it is likely not necessary to keep the recommended porosity of the scaffold for bone tissue replacement at about 90%, and it will also be clarified why this fact eliminates mechanical properties issue. Moreover, it is demonstrated that the size of an individual pore could be double the size of the recommended range between 0.2–0.35 mm without affecting the cell proliferation. Rapid prototyping technique based on Fused deposition modelling was used for the fabrication of designed scaffold structures. All the experiments were performed in order to show how to possibly solve certain limitations and issues that are currently reported by research workplaces on the field of scaffold bio-fabrication. These results should provide new valuable knowledge for further research.

Taher A Bahnasy, TS Amer, A Almahalawy et al.

A quasi-zero stiffness vibration isolator (QZSVI) is used in applications like precision instruments, aerospace, microelectronics manufacturing, and seismic isolation to protect sensitive equipment from low-frequency vibrations. Their key advantage lies in achieving near-zero stiffness, allowing for highly effective vibration attenuation while maintaining system stability. These passive systems are cost-effective and reliable, offering superior vibration isolation without the need for external power or active control. This work proposes the use of negative displacement, velocity, and cubic velocity feedback control techniques to enhance the QZSVI’s isolation performance. We found that the composite negative velocity and cubic velocity control (NVFC + NCVFC) is more effective with low cost compared to other types of controller (its effectiveness is about 94.8%). The approximate solutions (AS) of the controlling system of equations of motion (EOM) are acquired using a multiple-scales procedure (MSP) up to the second order, and it is subsequently validated numerically through the Runge–Kutta method (RKM) from the fourth-order. Modulation equations (ME) are obtained by exploring resonance instances and solvability conditions. Time history graphs and frequency response curves, generated via MATLAB and Wolfram Mathematica 13.2, are presented to analyze stability and steady-state solutions. It is investigated how altering the parameters affects the system amplitude. Poincaré maps, Lyapunov exponent spectra (LEs), and bifurcation diagrams are presented to illustrate the system’s diverse behavior patterns. Furthermore, the transmissibility of force, displacement, and acceleration is computed and displayed. A QZSVI minimizes low-frequency vibrations, making it ideal for precision applications in metrology, automotive, aerospace, civil engineering, medical equipment, and renewable energy. It achieves superior damping, ensuring high stability and precision.

S. Gayathri Devi, Ratnala Venkata Siva Harish, N. Nalini et al.

Human activity recognition (HAR) stands as a vital nexus in the synthesis of healthcare, sports analytics, and human–computer interaction. This research introduces a groundbreaking approach to HAR by amalgamating the multidimensional strengths of quaternion algebra with the temporal sensitivity of recurrent neural networks, birthing the “Human Activity Recognition Utilizing Quaternion-Based Recurrent Neural Networks (QRNNs)” model. This innovative fusion targets the inherent challenges of high-dimensionality and temporal sequencing posed by wearable sensor data. The proposed QRNN model showcased promising results, achieving an accuracy rate of 98.46% after 20 training epochs, marking a significant advancement in HAR's state-of-the-art. The experimental results showcase the effectiveness and improved accuracy of HAR models with the utilization of quaternion algebra. Overall, this study offers an innovatiove way for wearable technology and human−machine synergy by ensuring an advanced mathematical and statistical framework for perceptual human activity identification.

Kosei Tsuji, Ichiro Maruta, Kenji Fujimoto et al.

Nonlinear Model Predictive Control (NMPC) offers a powerful approach for controlling complex nonlinear systems, yet faces two key challenges. First, accurately modeling nonlinear dynamics remains difficult. Second, variables directly related to control objectives often cannot be directly measured during operation. Although high-cost sensors can acquire these variables during model development, their use in practical deployment is typically infeasible. To overcome these limitations, we propose a Predictive Virtual Sensor Identification (PVSID) framework that leverages temporary high-cost sensors during the modeling phase to create virtual sensors for NMPC implementation. We validate PVSID on a Two-Degree-of-Freedom (2-DoF) direct-drive robotic arm with complex joint interactions, capturing tip position via motion capture during modeling and utilize an Inertial Measurement Unit (IMU) in NMPC. Experimental results show our NMPC with identified virtual sensors achieves precise tip trajectory tracking without requiring the motion capture system during operation. PVSID offers a practical solution for implementing optimal control in nonlinear systems where the measurement of key variables is constrained by cost or operational limitations.

Jackson G. Ernesto, Eugenio B. Castelan, Walter Lucia

This paper presents a technique for designing output feedback controllers for constrained linear parameter-varying systems that are subject to persistent disturbances. Specifically, we develop an incremental parameter-varying output feedback control law to address control rate constraints, as well as state and control amplitude constraints. The proposal is based on the concept of robust positively invariant sets and applies the extended Farkas' lemma to derive a set of algebraic conditions that define both the control gains and a robust positively invariant polyhedron that satisfies the control and state constraints. These algebraic conditions are formulated into a bilinear optimization problem aimed at determining the output feedback gains and the associated polyedral robust positively invariant region. The obtained controller ensures that any closed-loop trajectory originating from the polyhedron converges to another smaller inner polyhedral set around the origin in finite time, where the trajectory remains ultimately bounded regardless of the persistent disturbances and variations in system parameters. Furthermore, by including the sizes of the two polyhedral sets inside the objective function, the proposed optimization can also jointly enlarge the outer set while minimizing the inner one. Numerical examples are presented to demonstrate the effectiveness of our proposal in managing the specified constraints, disturbances, and parameter variations.

Dongdong Li, Jiuxiang Dong

This paper proposes two cooperative optimal output tracking (COOT) algorithms based on policy iteration (PI) for discrete-time multi-agent systems with unknown model parameters. First, we establish a stabilizing PI framework that can start from any initial control policy, relaxing the dependence of traditional PI on the initial stabilizing control policy. Then, another efficient and equivalent Q-learning framework is developed, which is shown to require only less system data to get the same results as the stabilizing PI. In the two frameworks, the stabilizing control policy is obtained by gradually iterating the stabilizing virtual system to the actual feedback closed-loop system. Two explicit schemes for adjusting the iteration step-size/coefficient are designed and their stability is analyzed. Finally, the COOT is realized by a distributed feedforward-feedback controller with learned optimal gains. The proposed algorithms are validated by simulation.

Marcelo Jacinto, Rita Cunha

This work addresses the problem of designing a visual servo controller for a multirotor vehicle, with the end goal of tracking a moving spherical target with unknown radius. To address this problem, we first transform two bearing measurements provided by a camera sensor into a bearing-angle pair. We then use this information to derive the system's dynamics in a new set of coordinates, where the angle measurement is used to quantify a relative distance to the target. Building on this system representation, we design an adaptive nonlinear control algorithm that takes advantage of the properties of the new system geometry and assumes that the target follows a constant acceleration model. Simulation results illustrate the performance of the proposed control algorithm.

Zirui Li, Stephan Husung, Haoze Wang

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

Luca Ballotta, Juncal Arbelaiz, Vijay Gupta et al.

We study optimal proportional feedback controllers for spatially invariant systems when the controller has access to delayed state measurements received from different spatial locations. We analyze how delays affect the spatial locality of the optimal feedback gain leveraging the problem decoupling in the spatial frequency domain. For the cases of expensive control and small delay, we provide exact expressions of the optimal controllers in the limit for infinite control weight and vanishing delay, respectively. In the expensive control regime, the optimal feedback control law decomposes into a delay-aware filtering of the delayed state and the optimal controller in the delay-free setting. Under small delays, the optimal controller is a perturbation of the delay-free one which depends linearly on the delay. We illustrate our analytical findings with a reaction-diffusion process over the real line and a multi-agent system coupled through circulant matrices, showing that delays reduce the effectiveness of optimal feedback control and may require each subsystem within a distributed implementation to communicate with farther-away locations.

Christof Tinnes, Alisa Welter, Sven Apel

Modeling structure and behavior of software systems plays a crucial role in the industrial practice of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving software models with recommendations for model completions is still an open problem, though. In this paper, we explore the potential of large language models for this task. In particular, we propose an approach, RAMC, leveraging large language models, model histories, and retrieval-augmented generation for model completion. Through experiments on three datasets, including an industrial application, one public open-source community dataset, and one controlled collection of simulated model repositories, we evaluate the potential of large language models for model completion with RAMC. We found that large language models are indeed a promising technology for supporting software model evolution (62.30% semantically correct completions on real-world industrial data and up to 86.19% type-correct completions). The general inference capabilities of large language models are particularly useful when dealing with concepts for which there are few, noisy, or no examples at all.

Jiashou Wang, Guiyan Zhao, Zhongtai Li et al.

Multi-source data has received increasing attention due to its excellent performance in clustering tasks. However, existing multi-source data clustering methods utilize shallow graph learning methods to model similarity graphs of multi-source data, and ignore the importance of weak connections between samples within the same cluster. Thus, these connections fail to be explored in the global similarity graph of multi-source data. In this paper, we proposed a unified representation aggregated from graph and global features (UGGF) method for multi-source data clustering. Specifically, it utilizes the cross-source graph diffusion process to preserve invariant connections in multi-source similarity graphs and preserve weak connections between samples through their connection relationships between different sources. Furthermore, inspired by self-attention, the encoder of transformers is used to learn multi-source global feature representation. Then, global features and similarity graphs are integrated to comprehensively explore the multi-source data, aiming to obtain a unified representation for the final clustering task. The proposed UGGF method is validated on four benchmark datasets. Extensive experimental results demonstrate the superiority of the proposed method compared with other state-of-the-art multi-source data clustering methods.

Jun Wang, Zijian Yang, Wanqi Sun et al.

As a kind of good damping material, viscoelastic material is widely used in machinery, civil engineering, and other fields. In this paper, the viscoelasticity of the system is described by fractional differentiation. The dynamic response of a unilateral vibro-impact system with a viscoelastic oscillator under joint random excitation is studied, in which joint random excitation is composed of additive and multiplicative white noise. The fractional-order derivative was calculated based on Caputo’s definition, and the fractional derivative was equivalent to the corresponding linear damping force and linear restoring force. As a result, a new random system without fractional-order terms was obtained. A non-smooth transformation was introduced, which was equivalent to the original system to a new system without a velocity jump. The steady-state probability density functions of fractional-order vibro-impact systems under joint random excitation are solved by using the random average method and non-smooth transformation. In addition, the effects of parameters on the steady-state response of the system are analyzed.

YANG Linlin, SHEN Hua, LIN Youbin

The all-vanadium redox flow technology has garnered significant attention in the energy storage field, due to its attributes of high safety, high reliability, environmental friendliness, and power-capacity decoupling. Within all-vanadium redox flow energy storage setups, the control system is deemed the core, evolving towards enhanced integration, precision, and intelligence. This paper seeks to elevate these benchmarks and advance technological levels in the localization of this system. Based on a review of technologies related to the entire control system, this study conducted an in-depth analysis concerning key parameters and control logic. Addressing some key concerns within the control system, solutions were proposed, including temperature control, operational modes, and state-of-charge (SOC) estimation. The subsequent study focused on designing architecture for monitoring and managing all-vanadium redox flow energy storage systems through a modular approach, aiming at a more adaptable, elastic, and scalable control system. Furthermore, the developed control system was verified through practical implementation in commercial energy storage and applications under grid-connected environments. Based on the study outcomes, this paper also explores potential future technological trends for all-vanadium redox flow energy storage control systems in various aspects such as operation, maintenance, losses, and equilibrium.

Ruomao Liu, Yuxuan Xiang, Zihan Wei et al.

Untethered magnetic millirobots with a characteristic length of a few millimeters can be wirelessly controlled. They exhibit promising potential in a wide variety of applications, particularly for tasks in clinic workspaces. However, magnetically controlling these robots is counter‐intuitive and requires a steep learning curve, hindering their wide adoption. Herein, a computer‐aided teleoperation platform is developed to operate a soft millirobot, with its feedback control being conducted behind‐the‐scenes, bridging the user's inputs directly with the millirobot's actions to offer an intuitive control. This system enables untrained users to conveniently control the position and actions of the millirobot inside a human stomach phantom by pointing‐and‐clicking on a real‐time video monitor or using a keyboard. The platform automatically materializes the user's instructions by maneuvering a robotic arm with a tip‐mounted magnet to exert a magnetic field to induce the desired response from the millirobot. Experiments show that the system allows the user to intuitively operate the millirobot and deliver its cargo without splitting their attention to monitor the workspace or to calculate the constantly changing control parameters. This platform can lower the barrier for healthcare practitioners without engineering expertise to adopt miniature robotic systems into their workflow and realize these systems’ promising potential.

K. S. Jishnu, B. Arthi

The rise of cyber threats, particularly URL-based phishing attacks, has tarnished the digital age despite its unparalleled access to information. These attacks often deceive users into disclosing confidential information by redirecting them to fraudulent websites. Existing browser-based methods, predominantly relying on blacklist approaches, have failed to effectively detect phishing attacks. To counteract this issue, we propose a novel system that integrates a deep learning model with a user-centric Chrome browser extension to detect and alert users about potential phishing URLs instantly. Our approach introduces a Knowledge Distilled ELECTRA model for URL detection and achieves remarkable performance metrics of 99.74% accuracy and a 99.43% F1-score on a diverse dataset of 450,176 URLs. Coupled with the browser extension, our system provides real-time feedback, empowering users to make informed decisions about the websites they visit. Additionally, we incorporate a user feedback loop for continuous model enhancement. This work sets a precedent by offering a seamless, robust, and efficient solution to mitigate phishing threats for internet users.

Manuel Klädtke, Moritz Schulze Darup, Daniel E. Quevedo

Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct data-driven predictive control (FCS-DPC) counterpart has received relatively limited attention. In this paper, we introduce a novel reformulation of a commonly used DPC scheme that allows for the application of a modified sphere decoding algorithm, known for its efficiency and prominence in FCS-MPC applications. We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.

Kanghong Shi, Ian R. Petersen

We introduce a hybrid control system called a hybrid integrator-gain system (HIGS) based integral resonant controller (IRC) to stabilize negative imaginary (NI) systems. A HIGS-based IRC has a similar structure to an IRC, with the integrator replaced by a HIGS. We show that a HIGS-based IRC is an NI system. Also, for a SISO NI system with a minimal realization, we show there exists a HIGS-based IRC such that their closed-loop interconnection is asymptotically stable. Also, we propose a proportional-integral-double-integral resonant controller and a HIGS-based proportional-integral-double-integral resonant controller, and we show that both of them can be applied to asymptotically stabilize an NI system. An example is provided to illustrate the proposed results.

Shaoxiong Ma, Qing Tian, Chao Zou et al.

The demand for urban transport is increasing globally, and urban rail transit is an important infrastructure for meeting this demand. The objectives of this study were to effectively control and prevent all types of risks in the construction of metro projects and improve the quality and safety control of urban metro project construction. First, 20 index factors were selected from the five dimensions of “man–machinery–materials–methods–environment” and constructed an index system for assessing urban metro construction quality risks. Second, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were used to comprehensively evaluate the construction quality risks of subway projects, and the weights of the secondary indices were determined. Finally, the importance of secondary indicators was evaluated using the integrated AHP–FCE method, and the model was applied to engineering practice for validation. The results indicated that the comprehensive AHP–FCE method has good adaptability and rationality and has practical application value for metro project construction quality and safety risk assessment. It can help prevent urban metro construction quality accidents and provides a novel idea for metro project construction quality risk assessment.

ZHANG Yanqiu, LI Denggen, WANG Min'an et al.

In order to improve the operational capability of automatic train supervision (ATS), reduce the software and hardware cost, a dispatch path search method is proposed to linked devices such as platforms and signals. Based on the station map data and topology theory, the proposed method acquires the connection relationship of the platform in a semi-automatic or fully automatic manner, and the connection data of the platform is stored in the data structure of the adjacent matrix. The path search rule is designed, in which both depth-first search (DFS) and breadth-first search(BFS) are supported to obtain the path group. Automatic or manual methods to meet the project requirements are also proposed. Take the platform as an application case, the method is adapted to train dispatcher and route trigger function related to paths of linked devices. The path group can be reloaded by ATS subsystem, which can improve the ATS performance effectively. The method has been applied to Changsha urban rail transit Line 3 and Line 4, Wuxi urban rail transit Line 4, to support plan editing and train scheduling, and achieves good results, such as the generation time of station map can reduce over 70%,incidental jamming of the route trigger is eliminated when there are more trains,and route files can be generated automatically within minutes (original manual generation took several days).

K. Siva Agora Sakthivel Murugan, Marsaline Beno, R. Sankar et al.

In micro-grids, energy management is described as an information and control system that assures that both the generating and distribution systems deliver electricity at the lowest operating costs. Renewable energy sources (RESs), including electric vehicles (EVs), can be successfully used and carbon emissions reduced by establishing a DC multi-microgrid system (MMGS), which includes renewable energy sources (RESs) and the distribution network. A Multi-Microgrid based Energy Management (MM-GEM) system is suggested to increase the economics of MMGS and minimize the distribution network's network loss. MMG is a network of dispersed generators, energy storage, and adjustable loads in a distribution system that is linked. Furthermore, its operation is deconstructed to reduce communication and control costs with the decentralized structure. “Aside from enhancing system resilience, the MMGEMS substantially impacts energy efficiency, power quality, and dependability". Typical MMGEMS functionality and architecture are shown in detail. This is followed by examining current and developing technologies for monitoring and interacting with data among the MMG clusters. In addition, a wide range of MMG energy planning and control systems for interactive energy trading, multi-energy management, and resilient operations are fully examined and researched. The economic effect of the EVs’ energy transfer over time and place is examined.