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

Teerapong Poltue, Chao Zhang, Frédéric Demoly et al.

Active composite (AC) plates, composed of active and passive materials, can undergo complex shape transformations when stimulated. Leveraging 4D printing—which combines additive manufacturing with stimuli‐responsive materials—digitally encoded design patterns offer flexibility in shape morphing. However, performing inverse design, i.e., determining the pattern to achieve a desired shape, remains challenging due to the vast design space. Recently, machine learning (ML) has been applied to inverse design tasks with promising results. Nevertheless, these approaches require large datasets, and even then, inverse design remains difficult, often demanding multiple strategies and trials to obtain optimal results. To address these challenges, this work introduces an iterative data curation strategy combined with transfer learning. This method ensures that newly curated data is nonredundant and distinct from existing datasets, reducing the required training data by a factor of eight while maintaining performance. Additionally, ML models are integrated with a genetic algorithm (ML‐GA) to further fine‐tune the generated design patterns. The results show that ML‐GA enhances accuracy in achieving the desired shape while reducing computational effort. This framework offers an efficient and scalable approach for inverse design, reducing data needs and improving performance, making it a valuable tool for AC plate design and 4D printing.

Jingyi Kang, Ye Wang, Jiayuan Liu et al.

Steerable catheters offer significant advantages over conventional catheters, including enhanced control, stability, and accessibility, which reduce operational complexity, fluoroscopy time, and radiation exposure, positioning them as a promising advancement for vascular interventional procedures. Herein, a novel steerable catheter is presented, featuring a hydraulically actuated, soft, steerable tip that allows for real‐time visualization in X‐ray imaging. To optimize performance, several silicone materials were evaluated for their mechanical properties, resulting in a soft tip design with a diameter of 2.6 mm. The tip incorporates an internal tool channel and supports a large bending angle of 180°. The tip demonstrates an average response time of 1.141 s (±0.750 s), a maximum output force of 0.145 N (±0.001 N), and a maximum radial expansion of 1.121 (±0.006). A steering kinematic model of the catheter tip is developed to simulate its movement. The catheter tip's real‐time shape and position information are obtained through intelligent segmentation and neighborhood‐based endpoint detection methods, assisting the surgeon during superselective procedures. The catheter's visibility and flexibility are validated in a live porcine model, demonstrating its potential for future use in interventional procedures.

Mikhail V. Astakhov, Ekaterina V. Slavkina

Introduction. A promising approach to the machine design is the development of adaptive design structures compensating for external influences by changing the stress-strain state of their elements. However, these structures have not been widely used in agricultural machinery design because of the type of loading and the specific of transported loads that cause intense corrosion and abrasive damage. Aim of the study. The study is aimed at analyzing the methods for changing the strength characteristics of the structures of parts and assembly units of agricultural machinery during their operation and at developing methods for determining their stress-strain state to use in the design control algorithm. Materials and methods. As an example, we have examined the side of a vehicle body. Based on the principles of automatic control theory, the load-carrying structure is designed as a multilayer rectangular plate made of a polymer composite material. The inner layers are honeycomb filled with a non-Newtonian dilatant fluid. The filler performs the function of a compensator for external impacts. There were used the methods of mathematical modeling based on the constructing boundary value problems of the statics of multilayer thin plates, and plates on an elastic foundation, and studying their stress-strain state by solving analytically differential equations expressed in displacements. Results. There was developed an algorithm for determining the stress-strain state of a multilayer rectangular composite plate with discrete support under a normal distributed load taking into account impact disturbances. There were determined maximum bending stresses and the stresses arising from the braking of a rigid body moving with acceleration by the inner part of the plate. Discussion and Conclusion. In the study, there have been analyzed existing structural materials, technologies and technical means used to compensate for control action arising from the machine operation. There has been developed the methodology for transitioning from traditional designs to advanced models featuring adaptive elements, which change their stress-strain state depending on changes in external load. There has been theoretically substantiated a multilayer composite vehicle body side structure, which can withstand not only distributed forces but also impacts. Its material consumption is significantly reduced (several times) compared to steel, and its service life is increased due to the chemical resistance of the materials used in its manufacture. This design approach allows the proposed methodology to be used for a wide range of applications in the production of new agricultural equipment.

Lorenzo Zino, Mattia Boggio, Deborah Volpe et al.

We deal with controlling the spread of an epidemic disease on a network by isolating one or multiple locations by banning people from leaving them. To this aim, we build on the susceptible-infected-susceptible and the susceptible-infected-removed discrete-time network models, encapsulating a control action that captures mobility bans via removing links from the network. Then, we formulate the problem of optimally devising a control policy based on mobility bans that trades-off the burden on the healthcare system and the social and economic costs associated with interventions. The binary nature of mobility bans hampers the possibility to solve the control problem with standard optimization methods, yielding a NP-hard problem. Here, this is tackled by deriving a Quadratic Unconstrained Binary Optimization (QUBO) formulation of the control problem, and leveraging the growing potentialities of quantum computing to efficiently solve it.

G. Arun, C. N. Marimuthu

Big data analytics is a complex exploratory process to uncover hidden data information from vast collections of data. It often provides enormous information from diverse sources and the use of analytics provides confined knowledge from the collected noisy data. In the case of diabetes data, there exist a massive collection of patient data that relates to significant information on patient health and its critical nature. In order of validating and analysing the data to get desired information about a patient and their health risk from the vast collection of data, the study uses bigdata based deep learning analytics. This study uses a Deep Learning Model namely capsule network (CapsNet) is executed on a MapReduce framework. The CapsNet present in the MapReduce framework enables the classification of instances via proper regulations. This model after suitable training with the training dataset enables optimal classification of instances to detect the nature of the risk of a patient. The validation conducted on the test dataset shows that the proposed CapsNets-based MapReduce model obtains increased accuracy, recall, and F-score than the conventional MapReduce and deep learning models.

LIANG Xifan, WANG Yue, FU Jianguo et al.

DC microgrid systems based on hybrid photovoltaic-electrical energy storage systems for power generation are widely used in new energy cruise ships. For the problem of power supply voltage unbalance arising when a daily load power supply converter is connected to an unbalanced load or single-phase load, this paper proposes a dead-beat predictive voltage control strategy based on positive and negative sequence separation of output voltage. This strategy can realize control of positive and negative sequence components of the power supply converter voltage under the positive and negative sequence coordinate systems respectively, to ensure the three-phase voltage balance of the power supply converter, enhance the anti-interference ability of the system against unbalanced load and avoid additional power quality control devices. The simulation results show that, after the proposed control strategy is adopted, the voltage unbalance of the daily load power supply converter is reduced from 28% to less than 0.5%, which verifies the effectiveness of the proposed control algorithm .

Robin Strässer, Julian Berberich, Frank Allgöwer

In this paper, we propose a novel controller design approach for unknown nonlinear systems using the Koopman operator. In particular, we use the recently proposed stability- and feedback-oriented extended dynamic mode decomposition (SafEDMD) architecture to generate a data-driven bilinear surrogate model with certified error bounds. Then, by accounting for the obtained error bounds in a controller design based on the bilinear system, one can guarantee closed-loop stability for the true nonlinear system. While existing approaches over-approximate the bilinearity of the surrogate model, thus introducing conservatism and providing only local guarantees, we explicitly account for the bilinearity by using sum-of-squares (SOS) optimization in the controller design. More precisely, we parametrize a rational controller stabilizing the error-affected bilinear surrogate model and, consequently, the underlying nonlinear system. The resulting SOS optimization problem provides explicit data-driven controller design conditions for unknown nonlinear systems based on semidefinite programming. Our approach significantly reduces conservatism by establishing a larger region of attraction and improved data efficiency. The proposed method is evaluated using numerical examples, demonstrating its advantages over existing approaches.

Tongxin Li, Ruixiao Yang, Guannan Qu et al.

Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approximations of nonlinear dynamics. We study the problem of certifying a black-box control policy with stability using model-based advice for nonlinear control on a single trajectory. We first show a general negative result that a naive convex combination of a black-box policy and a linear model-based policy can lead to instability, even if the two policies are both stabilizing. We then propose an <italic>adaptive <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula>-confident policy</italic>, with a coefficient <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> indicating the confidence in a black-box policy, and prove its stability. With bounded nonlinearity, in addition, we show that the adaptive <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula>-confident policy achieves a bounded competitive ratio when a black-box policy is near-optimal. Finally, we propose an online learning approach to implement the adaptive <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula>-confident policy and verify its efficacy in case studies about the Cart-Pole problem and a real-world electric vehicle (EV) charging problem with covariate shift due to COVID-19.

V. Selvakumar, C. Akila

Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage.

Yujie Wang, Xiangru Xu

This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control-input matrices. The method combines control barrier functions and adaptive laws to generate a safe controller through a nonlinear program with an explicitly given closed-form solution. The proposed approach verifies the non-emptiness of the admissible control set independently of online parameter estimations, which can ensure the safe controller is singularity-free. A data-driven algorithm is also developed to improve the performance of the proposed controller by tightening the bounds of the unknown parameters. The effectiveness of the control scheme is demonstrated through numerical simulations.

Johannes Köhler, Matthas A. Müller, Frank Allgöwer

This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further research.

Eli Bacher-Chong, Mostafa Ali Ayubirad, Zeng Qiu et al.

This paper presents a hierarchical multivariable control and constraint management approach for an air supply system for a proton exchange membrane fuel cell (PEMFC) system. The control objectives are to track desired compressor mass airflow and cathode inlet pressure, maintain a minimum oxygen excess ratio (OER), and run the system at maximum net efficiency. A multi-input multi-output (MIMO) internal model controller (IMC) is designed and simulated to track flow and pressure set-points, which showed high performance despite strongly coupled plant dynamics. A new set-point map is generated to compute the most efficient cathode inlet pressure from the stack current load. To enforce OER constraints, a novel reference governor (RG) with the ability to govern multiple references (the cascade RG) and the ability to speed up as well as slow down a reference signal (the cross-section RG) is developed and tested. Compared with a single-input single-output (SISO) air-flow control approach, the proposed MIMO control approach shows up to 7.36 percent lower hydrogen fuel consumption. Compared to a traditional load governor, the novel cascaded cross-section RG (CC-RG) shows up to 3.68 percent less mean absolute percent error (MAPE) on net power tracking and greatly improved worst-case OER on realistic drive-cycle simulations. Control development and validations were conducted on two fuel cell system (FCS) models, a nonlinear open-source model and a proprietary Ford high-fidelity model

Yu Zheng, Sridhar Babu Mudhangulla, Olugbenga Moses Anubi

Systematic attack design is essential to understanding the vulnerabilities of cyber-physical systems (CPSs), to better design for resiliency. In particular, false data injection attacks (FDIAs) are well-known and have been shown to be capable of bypassing bad data detection (BDD) while causing targeted biases in resulting state estimates. However, their effectiveness against moving horizon estimators (MHE) is not well understood. In fact, this paper shows that conventional FDIAs are generally ineffective against MHE. One of the main reasons is that the moving window renders the static FDIA recursively infeasible. This paper proposes a new attack methodology, moving-horizon FDIA (MH-FDIA), by considering both the performance of historical attacks and the current system's status. Theoretical guarantees for successful attack generation and recursive feasibility are given. Numerical simulations on the IEEE-14 bus system further validate the theoretical claims and show that the proposed MH-FDIA outperforms state-of-the-art counterparts in both stealthiness and effectiveness. In addition, \textcolor{blue}{an experiment on} a path-tracking control system of an autonomous vehicle shows the feasibility of the MH-FDIA in real-world nonlinear systems.

Ying Zhan, Austin Fergusson, Lacey R. McNally et al.

Bacteria‐mediated drug delivery systems comprising nanotherapeutics conjugated onto bacteria synergistically augment the efficacy of both therapeutic modalities in cancer therapy. Nanocarriers preserve therapeutics’ bioavailability and reduce systemic toxicity, while bacteria selectively colonize the cancerous tissue, impart intrinsic and immune‐mediated antitumor effects, and propel nanotherapeutics interstitially. The optimal bacteria–nanoparticle (NP) conjugates will carry the maximal NP load with minimal motility speed hindrance for effective interstitial distribution. Furthermore, a well‐defined and repeatable NP attachment density distribution is crucial to determining these biohybrid systems’ efficacious dosage and robust performance. Herein, our nanoscale bacteria‐enabled autonomous delivery system (NanoBEADS) platform is utilized to investigate the effects of assembly process parameters of mixing method, volume, and duration on NP attachment density and repeatability. The effect of linkage chemistry and NP size on NP attachment density, viability, growth rate, and motility of NanoBEADS is also evaluated. It is shown that the linkage chemistry impacts NP attachment density while the self‐assembly process parameters affect the repeatability and, to a lesser extent, attachment density. Lastly, the attachment density affects NanoBEADS’ growth rate and motility in an NP size‐dependent manner. These findings will contribute to the development of scalable and repeatable bacteria–NP biohybrids for applications in drug delivery and beyond. An interactive preprint version of the article can be found here: https://www.authorea.com/doi/full/10.22541/au.163100509.93917936.

Seth Siriya, Jingge Zhu, Dragan Nešić et al.

We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.

Giannis Delimpaltadakis, Gabriel de A. Gleizer, Ivo van Straalen et al.

We present ETCetera, a Python library developed for the analysis and synthesis of the sampling behaviour of event triggered control (ETC) systems. In particular, the tool constructs abstractions of the sampling behaviour of given ETC systems, in the form of timed automata (TA) or finite-state transition systems (FSTSs). When the abstraction is an FSTS, ETCetera provides diverse manipulation tools for analysis of ETC's sampling performance, synthesis of communication traffic schedulers (when networks shared by multiple ETC loops are considered), and optimization of sampling strategies. Additionally, the TA models may be exported to UPPAAL for analysis and synthesis of schedulers. Several examples of the tool's application for analysis and synthesis problems with different types of dynamics and event-triggered implementations are provided.

Armin Nurkanović, Moritz Diehl

This article regards numerical optimal control of a class of hybrid systems with hysteresis using solely techniques from nonlinear optimization, without any integer variables. Hysteresis is a rate independent memory effect which often results in severe nonsmoothness in the dynamics. These systems are not simply Piecewise Smooth Systems (PSS); they are a more complicated form of hybrid systems. We introduce a time-freezing reformulation which transforms these systems into a PSS. From the theoretical side, this reformulation opens the door to study systems with hysteresis via the rich tools developed for Filippov systems. From the practical side, it enables the use of the recently developed Finite Elements with Switch Detection [Nurkanovic et al., 2022], which makes high accuracy numerical optimal control of hybrid systems with hysteresis possible. We provide a time optimal control problem example and compare our approach to mixed-integer formulations from the literature.

Hiroaki Mukaidani, Hua Xu, Weihua Zhuang

Abstract In this article, robust static output feedback (SOF) Nash games for a class of uncertain Markovian jump linear stochastic systems (UMJLSSs) are investigated, in which each player may have access to local/private SOF information. It is proved that the robust SOF Nash strategy set can be obtained by minimizing the upper bounds of the cost functions based on a guaranteed cost control mechanism. By using the Karush–Kuhn–Tucker (KKT) condition, the necessary conditions for the existence of the robust SOF Nash strategy set are established in terms of the solvability conditions of nonlinear simultaneous algebraic equations (NSAEs). A heuristic algorithm is developed to solve the NSAEs. Particularly, it is shown that the robust convergence of the heuristic algorithm is guaranteed by combining the Krasnoselskii–Mann (KM) iterative algorithm with a new convergence condition. Finally, a simple practical example is presented to show the reliability and usefulness of the proposed algorithm.

Annika Junker, Julia Timmermann, Ansgar Trächtler

Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.

Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo

Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting its sampling patterns. Finite-state abstractions, capturing the sampling behaviour of ETC systems, have proven promising in this respect. So far, such abstractions have been constructed for non-stochastic systems. Here, inspired by this framework, we abstract the sampling behaviour of stochastic narrow-sense linear periodic ETC (PETC) systems via Interval Markov Chains (IMCs). Particularly, we define functions over sequences of state-measurements and interevent times that can be expressed as discounted cumulative sums of rewards, and compute bounds on their expected values by constructing appropriate IMCs and equipping them with suitable rewards. Finally, we argue that our results are extendable to more general forms of functions, thus providing a generic framework to define and study various ETC sampling indicators.