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

Tiffany Thanhtruc Pham, Kenry

Macrophages play a central role in modulating different biological and physiological events. The behaviors and functions of macrophages may be regulated by a host of factors, including their viability, proliferation rate, and population density. Specifically, the population density of macrophages has been increasingly reported to be correlated with their activities. It is, however, still unclear if changes in macrophage population density will alter the biophysical attributes of these cells, notably their morphology. Herein, label‐free phase‐contrast microscopy is coupled with machine learning to interrogate the relationship between the population density and morphological features of macrophages. Through a systematic approach, variations in the morphological phenotypes of macrophages, which are dependent on their population density, are revealed. In parallel, through unsupervised clustering, the presence of single‐cell morphological heterogeneity within each macrophage population and subpopulation is elucidated. Next, discriminative morphological attributes which can be leveraged to distinguish between macrophages from different groups are identified through feature scoring. Finally, high‐performing explainable supervised machine learning algorithms that can be employed to predict the population density of macrophages based on their size and shape features are identified. This work is anticipated to offer a deeper understanding of the association between macrophage population density and morphologyas well as the potential use of morphological attributes as predictive metrics for analyzing cell populations.

Wanqiang Xi, Junzhi Lin, Ziyan Shao

In the field of mobile robot path planning, the artificial potential field (APF) method has been widely researched and applied due to its intuitiveness and efficiency. However, the APF algorithm often encounters challenges such as local minima and unreachable goals in complex environments. To address these issues, this paper proposes innovative path planning algorithm that integrates the advantages of the probabilistic roadmaps method (PRM), by introducing Sobol sampling and elliptical constraints to enhance PRM. The improved PRM not only reduces redundant nodes but also enhances the quality of sampling points. Furthermore, this paper uses the path nodes from the improved PRM as virtual target points for the APF algorithm, and effectively solves the inherent flaws of the APF algorithm through the segmented processing of the attractive force function and the introduction of a relative distance factor in the repulsive force function. Simulation results show that the algorithm reduces planning time, node count, and path length, demonstrate significant improvements in efficiency and performance. In addition, experiments with omnidirectional mobile robots further confirm the effectiveness and reliability of the algorithm in practical applications.

Luca Ballotta, Nicola Bastianello, Riccardo M. G. Ferrari et al.

In this paper, we address two practical challenges of distributed learning in multi-agent network systems, namely personalization and resilience. Personalization is the need of heterogeneous agents to learn local models tailored to their own data and tasks, while still generalizing well; on the other hand, the learning process must be resilient to cyberattacks or anomalous training data to avoid disruption. Motivated by a conceptual affinity between these two requirements, we devise a distributed learning algorithm that combines distributed gradient descent and the Friedkin-Johnsen model of opinion dynamics to fulfill both of them. We quantify its convergence speed and the neighborhood that contains the final learned models, which can be easily controlled by tuning the algorithm parameters to enforce a more personalized/resilient behavior. We numerically showcase the effectiveness of our algorithm on synthetic and real-world distributed learning tasks, where it achieves high global accuracy both for personalized models and with malicious agents compared to standard strategies.

J. Wehbeh, E. C. Kerrigan

Robust optimal or min-max model predictive control (MPC) approaches aim to guarantee constraint satisfaction over a known, bounded uncertainty set while minimizing a worst-case performance bound. Traditionally, these methods compute a trajectory that meets the desired properties over a fixed prediction horizon, apply a portion of the resulting input, and then re-solve the MPC problem using newly obtained measurements at the next time step. However, this approach fails to account for the fact that the control trajectory will be updated in the future, potentially leading to conservative designs. In this paper, we present a novel update-aware robust optimal MPC algorithm for decreasing horizon problems on nonlinear systems that explicitly accounts for future control trajectory updates. This additional insight allows our method to provably expand the feasible solution set and guarantee improved worst-case performance bounds compared to existing techniques. Our approach formulates the trajectory generation problem as a sequence of nested existence-constrained semi-infinite programs (SIPs), which can be efficiently solved using local reduction techniques. To demonstrate its effectiveness, we evaluate our approach on a planar quadrotor problem, where it clearly outperforms an equivalent method that does not account for future updates at the cost of increased computation time.

Lucas Cammann, Johannes Jäschke

Green hydrogen, produced via water electrolysis using renewable energy, is seen as a cornerstone of the energy transition. Coupling of renewable power supplies to water electrolysis processes is, however, challenging, as explosive gas mixtures (hydrogen in oxygen) might form at low loads. This has prompted research into gas purity control of such systems. While these attempts have shown to be successful in theoretical and practical studies, they are currently limited in that they only consider the gas purity at locations where composition measurements are available. As these locations are generally positioned downstream of the disturbance origin, this incurs considerable delays and can lead to undetected critical conditions. In this work, we propose the use of an Extended Kalman Filter (EKF) in combination with a simple process model to estimate and control the gas composition at locations where measurements are not available. The model uses noise-driven states for the gas impurity and is hence agnostic towards any mechanistic disturbance model. We show in simulations that this simple approach performs well under various disturbance types and can reduce the time spent in potentially hazardous conditions by up to one order of magnitude.

Akihito Hayano, Shigemasa Takai

Filip Kubik, Marcin Krysiak

Rapid development of Artificial Intelligence (AI) technologies in recent years has created new opportunities to address the growing challenges in the aviation industry. Machine learning and Deep Learning, particularly through Convolutional Neural Networks (CNNs), have advanced image recognition capabilities, enhancing inspection processespossibilities. This paper explores the integration of AI with drones to improve the precision, efficiency, and speed of inspections of airframe emphasizing the necessity of accurate equipment preparation and precise operational planning. The study demonstrates how AI algorithms can process high-resolution images and sensor data to identify and classify defects. The motivation for this paper is to address the critical need for more efficient inspection methods in aviation, driven by the industry's increasing demand for higher repair process throughput and stringent safety standards.

HUANG Jiade, LIU Yong, HU Yunqing et al.

Open-pit mines, characterized by complicated environmental conditions and unstructured roads, pose challenges for the path planning of mine trucks, marked by inefficiency due to long-distance search and the need to adhere to the left-hand traffic rules common in mining settings. This paper introduces an optimization algorithm for adaptive dual-layer search path planning based on the left-hand traffic rules in mining areas, aiming to improve the navigation performance of mine trucks within these areas while ensuring the path safety and the real-time planning. The upper-layer search, employing the rapidly-exploring random tree (RRT) algorithm and accommodating the environmental characteristics of mining areas, features adaptive adjustments of search step sizes, achieving efficient searches with larger step sizes in open areas, while detailed searches with smaller step sizes in narrow or winding areas. Moreover, the search process is further optimized by setting range constraints for left-hand driving, to prevent potential vehicle conflicts and facilitate rapid convergence of path search. The lower-layer search incorporates a hybrid A* algorithm, effectively narrowing search space and enhancing the real-time nature of path planning through a reward and penalty mechanism for path selection. Furthermore, Reeds-Shepp (RS) curve and an improved cubic spline curve are applied to smooth the resultant path, not only optimizing path curvature but also ensuring that the mine truck reaches the target location in an optimal posture. Further optimization through the gradient descent method enables the efficient generation of safe and smooth paths. Experimental results showed a 14.8% reduction in path generation time and a 0.011 m^-1 decrease in path curvature, demonstrating a significant enhancement in the navigation efficiency and safety during autonomous truck driving in mining areas.

ZENG Gaoping, WANG Lei

With the rapid development of urban rail transit in China, the demand for safe and intelligent operation is growing. According to the actual operation needs of urban rail train, this paper builds a real-time streaming data processing architecture for intelligent operation and maintenance adopting technologies such as big data, mobile internet, internet of things, and micro-services. This paper mainly expounds a portable, scalable and efficient processing method base on train real-time data, which realizes the functions of rapid data verification, analysis, storage, sharing, push, etc., and supports the application system to realize real-time train monitoring and emergency response and disposal guidance, train daily inspection items self-inspection and other functions. The proposed system mainly includes two parts: data platform and application platform. The data platform completes rapid processing of real-time data stream sent by the onboard device with the help of high-concurrency asynchronous IO framework, message queue, stream computing, storage engine and other big data processing components; efficient real-time interaction and flexible application of real-time data are realized with database, active data push and other technologies. The application platform uses real-time data of train to realize train-related demand functions, and effectively converts the data into actual business form, thereby improves the ability of personnel to perceive the running status of vehicle, reducing workload of artificial maintenance and operation, and ensuring the train high-quality operations.

Wenceslao Shaw Cortez, Xiao Tan, Dimos V. Dimarogonas

We propose a novel (Type-II) zeroing control barrier function (ZCBF) for safety-critical control, which generalizes the original ZCBF approach. Our method allows for applications to a larger class of systems (e.g. passivity-based) while still ensuring robustness, for which the construction of conventional ZCBFs is difficult. We also propose a locally Lipschitz continuous control law that handles multiple ZCBFs, while respecting input constraints, which is not currently possible with existing ZCBF methods. We apply the proposed concept for unicycle navigation in an obstacle-rich environment.

Lorenz Graf-Vlachy, Daniel Graziotin, Stefan Wagner

Context: Citations are a key measure of scientific performance in most fields, including software engineering. However, there is limited research that studies which characteristics of articles' metadata (title, abstract, keywords, and author list) are driving citations in this field. Objective: In this study, we propose a simple theoretical model for how citations come to be with respect to article metadata, we hypothesize theoretical linkages between metadata characteristics and citations of articles, and we empirically test these hypotheses. Method: We use multiple regression analyses to examine a data set comprising the titles, abstracts, keywords, and authors of 16,131 software engineering articles published between 1990 and 2020 in 20 highly influential software engineering venues. Results: We find that number of authors, number of keywords, number of question marks and dividers in the title, number of acronyms, abstract length, abstract propositional idea density, and corresponding authors in the core Anglosphere are significantly related to citations. Conclusion: Various characteristics of articles' metadata are linked to the frequency with which the corresponding articles are cited. These results partially confirm and partially go counter to prior findings in software engineering and other disciplines.

Shuo Yang, Shaoru Chen, Victor M. Preciado et al.

Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees. To address this issue, control barrier functions (CBFs) have been applied as a safety filter to monitor and modify the outputs of learning-based controllers in order to guarantee the safety of the closed-loop system. However, such modification can be myopic with unpredictable long-term effects. In this work, we propose a safe-by-construction NN controller which employs differentiable CBF-based safety layers, and investigate the performance of safe-by-construction NN controllers in learning-based control. Specifically, two formulations of controllers are compared: one is projection-based and the other relies on our proposed set-theoretic parameterization. Both methods demonstrate improved closed-loop performance over using CBF as a separate safety filter in numerical experiments.

Lin Xu, Baoye Song, Maoyong Cao

Smooth path planning is very important  to mobile robots with continuous-curvature constraint, but there are still some limitations and drawbacks on traditional planning approach. To deal with this problem, a new approach combined with parametric cubic Bezier curve (PCBC) and particle swarm optimization with adaptive delayed velocity (PSO-ADV), is developed to plan the smooth path of mobile robots. Unlike the traditional smooth path consisting of several linear and curve segments with discontinuous curvature at the joints, the smooth path composed of PCBC segments has equivalent curvature at the segment joints, thereby it is able to attain continuous curvature along the whole smooth path. In terms of the mathematical formulation of PCBC, the smooth path planning is essentially an optimization problem to seek the optimal control points and parameters of PCBC segments. To handle this intractable problem and some frequently encountered troubles (e.g. premature convergence and local trapping), a new PSO-ADV algorithm is developed by blending the term of adaptive delayed velocity, and its superiority can be confirmed by several simulation experiments. The new approach is finally applied to produce the smooth path with continuous-curvature constraint, and can achieve superior performance in comparison with traditional method.

Laurianne Delcor, Etienne Parizet, Julie Ganivet-Ouzeneau et al.

Vibrations contribute to helicopter’s ride comfort. This study aimed to determine the relationship between main rotor vertical excitations and discomfort. Fifty-three participants, seated on a helicopter seat fixed to a vibration test bench, evaluated the discomfort of vertical sinusoidal vibrations using a magnitude estimation procedure. Stimuli had a frequency between 15 and 30 Hz and a level between 0.32 and 3.16 m/s 2 . The average discomfort was shown related to vibration velocity using Steven’s power law, without any frequency dependence. The exponent depended on velocity and was 1.18 for higher velocities (approx. above 0.008 m/s) and 0.65 for velocities below that limit.

Weiya Chen, Tetsuo Sawaragi, Toshihiro Hiraoka

With the development of automated driving technologies, human factors involved in automated driving are gaining increasing attention for a balanced implementation of the convenience brought by the technology and safety risk in commercial vehicle models. One influential human factor is mental workload. In the take-over request (TOR) from autonomous to manual driving at level 3 of International Society of Automotive Engineers' (SAE) Levels of Driving Automation, the time window for the driver to have full comprehension of the driving environment is extremely short, which means the driver is under high mental workload. To support the driver during a TOR, we propose an adaptive multi-modal interface model concerning mental workload. In this study, we evaluated the reliability of only part of the proposed model in a driving-simulator experiment as well as using the experimental data from a previous study.

Yanan Jiang, Liangjian Hu, Jianqiu Lu et al.

Abstract This paper mainly investigates stabilization of hybrid stochastic differential equations (SDEs) via periodically intermittent feedback controls based on discrete‐time state observations with a time delay. First, by using the theory of M‐matrix and intermittent control strategy, we establish sufficient conditions for the stability of hybrid SDEs. Then, we prove the intermittent stabilization for a given unstable nonlinear hybrid SDE by comparison theorem. Two numerical examples are discussed to support our results of theoretical analysis.

Daojing Lin, Jingyi Wang, Niandong Jiao et al.

Flexible magnetic continuum robots (MCRs) can be fabricated in small dimensions with great manipulability as their tips can deflect under fields, without the need for complicated mechanical structures to govern control, and it helps improve operating conditions during retrograde intrarenal surgery (RIRS). However, the limited effective workspace of an electromagnet‐based magnetic navigation system (MNS) influences the practical applications. Herein, the method of steering a flexible MCR in the enlarged workspace of a common MNS for RIRS is presented. First, the field heterogeneity is parameterized to quantitatively analyze its influence on the motion of the MCR. Then, a kinematic model of the MCR is constructed, by coupling the heterogeneous‐field and Cosserat‐rod models, to predict its large deformation in the enlarged workspace with constraints. The model is validated with the maximum mean error of 0.53 ± 0.39 mm for the tip position in all the experiments. It is demonstrated that the effective workspace can be enlarged to 75% of the physical workspace. In addition, experiments in phantoms, which simulate two challenges during RIRS, are performed to prove its manipulability. This study enlarges the effective workspace of the MNS, which helps expand the practical application of the MCR.

Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine

Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential length as a Lyapunov-like function, its nonlinear stability analysis boils down to finding a suitable contraction metric that satisfies a stability condition expressed as a linear matrix inequality, indicating that many parallels can be drawn between well-known linear systems theory and contraction theory for nonlinear systems. Furthermore, contraction theory takes advantage of a superior robustness property of exponential stability used in conjunction with the comparison lemma. This yields much-needed safety and stability guarantees for neural network-based control and estimation schemes, without resorting to a more involved method of using uniform asymptotic stability for input-to-state stability. Such distinctive features permit the systematic construction of a contraction metric via convex optimization, thereby obtaining an explicit exponential bound on the distance between a time-varying target trajectory and solution trajectories perturbed externally due to disturbances and learning errors. The objective of this paper is, therefore, to present a tutorial overview of contraction theory and its advantages in nonlinear stability analysis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based and data-driven automatic control methods. In particular, we provide a detailed review of techniques for finding contraction metrics and associated control and estimation laws using deep neural networks.

Debasattam Pal, Shiva Shankar

A discrete multidimensional system is the set of solutions to a system of linear partial difference equations defined on the lattice $\Z^n$. This paper shows that it is determined by a unique coarsest sublattice, in the sense that the solutions of the system on this sublattice determine the solutions on $\Z^n$; it is therefore the correct domain of definition of the discrete system. In turn, the defining sublattice is determined by a Galois group of symmetries that leave invariant the equations defining the system. These results find application in understanding properties of the system such as controllability and autonomy, and in its order reduction.

Ethan N. Evans, Oswin So, Andrew P. Kendall et al.

We consider the optimal control problem of a general nonlinear spatio-temporal system described by Partial Differential Equations (PDEs). Theory and algorithms for control of spatio-temporal systems are of rising interest among the automatic control community and exhibit numerous challenging characteristic from a control standpoint. Recent methods focus on finite-dimensional optimization techniques of a discretized finite dimensional ODE approximation of the infinite dimensional PDE system. In this paper, we derive a differential dynamic programming (DDP) framework for distributed and boundary control of spatio-temporal systems in infinite dimensions that is shown to generalize both the spatio-temporal LQR solution, and modern finite dimensional DDP frameworks. We analyze the convergence behavior and provide a proof of global convergence for the resulting system of continuous-time forward-backward equations. We explore and develop numerical approaches to handle sensitivities that arise during implementation, and apply the resulting STDDP algorithm to a linear and nonlinear spatio-temporal PDE system. Our framework is derived in infinite dimensional Hilbert spaces, and represents a discretization-agnostic framework for control of nonlinear spatio-temporal PDE systems.