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

M. Stosch, Rui Oliveira, J. Peres et al.

Shaowen Miao, Jan Komenda, Aiwen Lai

Language diagnosability captures the capability of the system to detect faults based on observations. When a system is not diagnosable, supervisory control can be used to enforce its diagnosability to prevent the faults from occurring silently, known as the active diagnosis problem. Note that in the context of timed discrete-event systems, an observation contains not only the sequence of the events but also their time information. Therefore, two sequences consisting of the same events but with different occurrence time instants can still reveal the occurrence of faults. This fact motivates us to consider enforcing the diagnosability of a timed discrete-event system by regulating the occurrence time instants of certain controllable events. In this paper, we first construct a verifier for a time-interval automaton to verify its diagnosability. Then, based on the verifier, we enforce the diagnosability of a time-interval automaton by restricting the time intervals of certain controllable events and disabling some controllable events. Note to Practitioners—Fault diagnosis and active diagnosis play a critical role in ensuring the reliability, safety, and efficiency of systems across various industries, ranging from automotive and aerospace to healthcare and smart grids. Discrete-event systems, as general models for complex man-made systems, are well-studied for modeling digital computer systems in the above scenarios. Early detection and correction of faults contribute to improved performance, reduced downtime, and enhanced overall system functionality. This work investigates the active diagnosis problem, i.e., design a supervisor to enforce diagnosability, for discrete-event systems modeled by time-interval automata. The control policy combines the time and logical information of the system, thereby allowing the closed-loop systems to retain more of the original system behavior. Time-interval automata is a model that is not complicated but is closer to actual engineering systems than finite automata, providing new insights for control engineers in modeling and control.

Yujing Fu, Lijun Jiang, Jiafu Lin et al.

Based on the mechanism of human visual imaging, retinal projection display (RPD) directly projects images onto the retina, achieving sharp images without relying on the focal adjustment of human eye. However, the physiological phenomenon of eye movements makes it difficult to align the convergence point of image lights with the human pupil, especially when the viewer needs to wear a pair of vision‐corrected frame glasses, resulting in blurred images. To accommodate the movements of human eye, herein, a waveguide‐based RPD system with bidirectional extended eyebox is proposed, in which two‐layer holographic optical elements (HOEs) are designed as the image combiner to generate different eye reliefs along the visual axis of the human eye. Each layer of HOEs generates two horizontally arranged viewpoints, thus, achieving bidirectional eyebox expansion. Experimental results show that the proposed RPD system provides two sets of viewpoints with the eye reliefs of 11 and 12 mm, respectively, and obtains a 2 × 2 viewpoint array distributed horizontally and axially. Additionally, two sets of viewpoints can be switched to accommodate the different eyewear habits of viewers. The proposed RPD system enhances the adaptability of near‐eye display device to the human eye through the bidirectional eyebox expansion.

Chaoping Gui

Refrigerators bring convenience to people, but the noise they produce can also affect people’s lives. Refrigerator noise is dominated by low-frequency noise, and the active noise control method is more effective for this kind of noise. However, the existing active noise control methods for refrigerators do not take into account factors such as the limited space and complex structure of the refrigerator compressor room, and external interference signals will be introduced to affect the noise reduction performance during the error signal collection. Given that, this paper proposes the cascade notch feedback algorithm, which can well reduce the influence of external interference signals to better achieve the refrigerator noise reduction. The algorithm consists of the cascaded notch filtering algorithm and the feedback algorithm. The cascade notch filtering algorithm is formed by cascading the adaptive notch filtering algorithm, which is used to deal with the main frequency and harmonic noise generated by the refrigerator. The feedback algorithm consists of a robust algorithm for dealing with external interference signals introduced during error signal collection. The simulation experiment proves that the algorithm has advantages in terms of noise reduction and computational cost compared with the adaptive notch filtering algorithm and the improved algorithm. The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.

Babak Esmaeili, Nariman Niknejad, Hamidreza Modares

This paper presents a risk-aware safe reinforcement learning (RL) control design for stochastic discrete-time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk-informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together. Several advantages come along with this approach: 1) High-confidence safety can be certified without relying on a high-fidelity system model and using limited data available, 2) Myopic interventions and convergence to an undesired equilibrium can be avoided by deciding on the contribution of two stabilizing controllers, and 3) highly efficient and computationally tractable solutions can be provided by optimizing over a scalar decision variable and linear programming polyhedral sets. To learn safe controllers with a large invariant set, piecewise affine controllers are learned instead of linear controllers. To this end, the closed-loop system is first represented using collected data, a decision variable, and noise. The effect of the decision variable on the variance of the safe violation of the closed-loop system is formalized. The decision variable is then designed such that the probability of safety violation for the learned closed-loop system is minimized. It is shown that this control-oriented approach reduces the data requirements and can also reduce the variance of safety violations. Finally, to integrate the safe and RL controllers, a new data-driven interpolation technique is introduced. This method aims to maintain the RL agent's optimal implementation while ensuring its safety within environments characterized by noise. The study concludes with a simulation example that serves to validate the theoretical results.

Yazhou Xie

This article surveys the growing interest in utilizing Deep Learning (DL) as a powerful tool to address challenging problems in earthquake engineering. Despite decades of advancement in domain knowledge, issues such as uncertainty in earthquake occurrence, unpredictable seismic loads, nonlinear structural responses, and community engagement remain difficult to tackle using domain-specific methods. DL offers promising solutions by leveraging its data-driven capacity for nonlinear mapping, sequential data modeling, automatic feature extraction, dimensionality reduction, optimal decision-making, etc. However, the literature lacks a comprehensive review that systematically covers a consistent scope intersecting DL and earthquake engineering. To bridge the gap, the article first discusses methodological advances to elucidate various applicable DL techniques, such as multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), autoencoder (AE), transfer learning (TL), reinforcement learning (RL), and graph neural network (GNN). A thorough research landscape is then disclosed by exploring various DL applications across different research topics, including vision-based seismic damage assessment and structural characterization, seismic demand and damage state prediction, seismic response history prediction, regional seismic risk assessment and community resilience, ground motion (GM) for engineering use, seismic response control, and the inverse problem of system/damage identification. Suitable DL techniques for each research topic are identified, emphasizing the preeminence of CNN for vision-based tasks, RNN for sequential data, RL for community resilience, and unsupervised learning for GM analysis. The article also discusses opportunities and challenges for leveraging DL in earthquake engineering research and practice.

Jingang Jiang, Chuan Lin, Shuainan Xu et al.

Compared to conventional actuators, the soft ionic polymer–metal composite (IPMC) actuator has significant advantages in specific applications, and the mathematical model of IPMC actuators is essential to comprehending and applying IPMCs. Due to the inherent characteristics of IPMCs and the impact of the manufacturing and measurement processes, it is challenging to developa reliable model. This article provides a comprehensive overview of the developments in IPMC actuator modeling. In particular, three types of models are examined and contrasted: the nonphysical identification model, the partial‐physical model, and the physical‐based model. In order to comprehend the current state of numerous IPMC actuator models, the characteristics, evolution, and functions of each type of model are discussed. Afterward, the evolution of the IPMC actuators’ applications is discussed. Finally, promising research directions for IPMC actuator models are identified that can more effectively facilitate the development of IPMC‐based devices.

Luke Bhan, Peijia Qin, Miroslav Krstic et al.

Predictor feedback designs are critical for delay-compensating controllers in nonlinear systems. However, these designs are limited in practical applications as predictors cannot be directly implemented, but require numerical approximation schemes, which become computationally prohibitive when system dynamics are expensive to compute. To address this challenge, we recast the predictor design as an operator learning problem, and learn the predictor mapping via a neural operator. We prove the existence of an arbitrarily accurate neural operator approximation of the predictor operator. Under the approximated predictor, we achieve semiglobal practical stability of the closed-loop nonlinear delay system. The estimate is semiglobal in a unique sense - one can enlarge the set of initial states as desired, though this increases the difficulty of training a neural operator, which appears practically in the stability estimate. Furthermore, our analysis holds for any black-box predictor satisfying the universal approximation error bound. We demonstrate the approach by controlling a 5-link robotic manipulator with different neural operator models, achieving significant speedups compared to classic predictor feedback schemes while maintaining closed-loop stability.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

M. Moustapha, Pietro Parisi, Stefano Marelli et al.

System reliability analysis aims at computing the probability of failure of an engineering system given a set of uncertain inputs and limit state functions. Active-learning solution schemes have been shown to be a viable tool but as of yet they are not as efficient as in the context of component reliability analysis. This is due to some peculiarities of system problems, such as the presence of multiple failure modes and their uneven contribution to failure, or the dependence on the system configuration (e.g., series or parallel). In this work, we propose a novel active learning strategy designed for solving general system reliability problems. This algorithm combines subset simulation and Kriging/PC-Kriging, and relies on an enrichment scheme tailored to specifically address the weaknesses of this class of methods. More specifically, it relies on three components: (i) a new learning function that does not require the specification of the system configuration, (ii) a density-based clustering technique that allows one to automatically detect the different failure modes, and (iii) sensitivity analysis to estimate the contribution of each limit state to system failure so as to select only the most relevant ones for enrichment. The proposed method is validated on two analytical examples and compared against results gathered in the literature. Finally, a complex engineering problem related to power transmission is solved, thereby showcasing the efficiency of the proposed method in a real-case scenario.

Marco Maggipinto, A. Beghi, Gian Antonio Susto

In manufacturing industries, it is of fundamental importance to detect anomalies in production in order to meet the required quality goals and to limit the number of defective products that are accidentally delivered to the customers. Nevertheless, monitoring systems currently employed in production are typically very simple and rely on a set of univariate control charts that fail to capture the multivariate and complex nature of real-world industrial systems. In such context, Machine Learning (ML)-based approaches for Anomaly Detection (AD) have proven to be extremely effective in increasing anomalies detectability and, in general, in enhancing monitoring procedures. However, industrial data are typically very complex and not suitable to be fed directly to classical ML-based AD tools making feature extraction procedures a necessary step that unfortunately may lead to information loss and low scalability. Deep Learning, has proven very effective at learning useful representations of complex data in an automatic way. In this paper, we propose an AD pipeline that makes use of convolutional autoencoders to extract useful features from two-dimensional, non-image, data. We test our approach on real world Optical Emission Spectroscopy data that are typical of semiconductor manufacturing and we achieve improved performance over classical monitoring methods. Note to Practitioners—Advanced monitoring is one of the most important task in the context of Industry 4.0. Some of the main issues in developing Machine Learning-based solutions in industrial environment are: (i) the lack of reliable tagged data; (ii) the complexity of data structures present in real-world scenarios. In this paper we investigate unsupervised anomaly detection for 2-dimensional data in manufacturing environment: we provide an approach that exploit Deep Learning-based architecture for handling the data at hand. We show the effectiveness of the proposed approach in a real world case study related to optical emission spectroscopy data in semiconductor manufacturing process providing satisfactory classification accuracy.

Xiaoxiang Han, Qingmiao Chen, Qinyong Ma et al.

Abstract Environment perception is one of the most vital function for autonomous robots while performing complex tasks in dynamic environment. In binocular stereo vision algorithm, the calculation of disparity image depends on matching algorithm, and the unmatched points form depth holes in the image. When encountering obstacles with less texture, such as blank walls, large area deep holes will often appear. Although the existing filtering algorithm can solve the problem of small area deep hole, it cannot be applied to large area deep hole. To solve this problem, a depth hole optimization algorithm is proposed, which detects large area depth holes from the image globally, fills the depth holes according to the disparity value distribution, and optimizes the weighted least squares filtering effect. The experimental results show that the average time of the algorithm is only 15 ms.

TIAN Deqiang, JIANG Xuezhai, ZHANG Xiaofeng et al.

The display platform of train provides the train driver with the key data of the running train and receives control command input. Its operation reliability is very important for safe driving. In order to quickly locate and response to the fault of the display platform and reduce the impact of the fault on train operation, the paper proposes a scheme for real-time diagnosis and data synchronization of train display platform operation status based on redundant display platform with two display units. Firstly, the self-diagnosis of the display unit was realized to monitor and diagnose the equipment status in real time based on the application layer, and to quickly locate the problems such as application exit, system crash and anomalies in resource utilization. Secondly, based on the RS422 communication link, a reliable communication protocol was defined to achieve the mutual diagnosis and data synchronization of operation status between multiple display units, and solve the problems of self-diagnosis failure in abnormal state of the unit and failure to obtain key data of faulty unit. A function verification experiment was carried out based on the train redundant display platform to verify the effectiveness of this scheme. The experimental results indicate that on the one hand, this scheme provides comprehensive self-diagnosis and redundant display unit state mutual diagnosis from the application layer, providing a basis for fault analysis and localization. On the other hand, it provides a way for two display units to work coordinately through key data synchronization and storage functions.

ZHOU Zhenbang, WANG Yue, FU Jianguo et al.

Multi-source powered mining trucks are expected with broad market prospects, due to the advantages in energy conservation, environmental protection, flexibility and efficiency. The three-level DC/DC converter with excellent input and output characteristics is generally used as an on-board energy interaction device for energy distribution and management between various on-board power equipment and multiple functional loads. In addition to adaption to a variety of power supply modes and battery charging and discharging control, the converter needs to be configured with different control methods and adaptively switchable among different control methods for the diversity of loads; moreover, it is necessary to solve the neutral point voltage balancing under different modes. Taking the on-board DC/DC converter as the research object, this paper first analyzes energy flow, and proposes an energy management strategy for battery charging and discharging, power supply for various loads and feedback depending on current direction, and a unified neutral point voltage balancing strategy for the three-level DC/DC converter under different working conditions, forming a complete set of energy management methodology. The simulation results show that the DC/DC converter applied with this control strategy has a good neutral point voltage balancing effect under different working conditions.

Pedro Albertos, Manuel Berenguel, Matilde Santos

YUE Wei, LIN Jun, KANG Gaoqiang et al.

In the driverless transport and operation system of surface mine, the roadside perception system is used to assist driverless vehicles by providing road condition information. The driverless system currently applied in mine trucks realizes roadside perception based on the multi-sensor fusion technology, consisting of cameras, laser radars and millimeter wave radars. However, this system has several drawbacks, such as high system cost, a complicated structure and poor robustness. In this regard, this paper proposes a roadside perception approach based on an improved DeepSORT algorithm. This approach involves using cameras to acquire image data on vehicles and pedestrians in the mine, which are accurately identified by the YOLOv5s algorithm. Then, the improved DeepSORT algorithm tracks the identified objects in real-time, enabling statistical analysis to provide various functions, including vehicle traffic statistics, abnormal parking detection and pedestrian intrusion detection. The proposed approach was tested at the No. 8 intersection of Xiwan Surface Mine of Shaanxi Shenyan Coal Co., Ltd. The results show that using a single sensor approach can effectively achieve the recognition and tracking of vehicles and pedestrians at mine intersection, reduce the complexity of the roadside perception system and save costs compared to the roadside perception technology based on multi-sensor fusion.

Jie JW Wu

Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models, frameworks and tools that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.

Shawn Priore, Meeko Oishi

While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient methods to handle non-Gaussian processes. In this paper, we develop a method for solving chance constrained stochastic optimal control problems for linear time-invariant systems with general additive disturbances with finite moments and unimodal chance constraints. We propose an open-loop control scheme for multi-vehicle planning, with both target sets and collision avoidance constraints. Our method relies on the one-sided Vysochanskij-Petunin inequality, a tool from statistics used to bound tail probabilities of unimodal random variables. Using the one-sided Vysochanskij-Petunin inequality, we reformulate each chance constraint in terms of the expectation and standard deviation. While the reformulated bounds are conservative with respect to the original bounds, they have a simple and closed form, and are amenable to difference of convex optimization techniques. We demonstrate our approach on a multi-satellite rendezvous problem.

Brooks A. Butler, Philip E. Paré

As modern systems become ever more connected with complex dynamic coupling relationships, developing safe control methods becomes paramount. In this paper, we discuss the relationship of node-level safety definitions for individual agents with local neighborhood dynamics. We define a collaborative control barrier function (CCBF) and provide conditions under which sets defined by these functions will be forward invariant. We use collaborative node-level control barrier functions to construct a novel \edit{decentralized} algorithm for the safe control of collaborating network agents and provide conditions under which the algorithm is guaranteed to converge to a viable set of safe control actions for all agents. We illustrate these results on a networked susceptible-infected-susceptible (SIS) model.