Adailton Gomes PereiraGustavo Franco BarbosaMoacir Godinho FilhoSidney Bruce Shiki...
2522-2534页
查看更多>>摘要：Additive manufacturing (AM) revolutionizes product creation with its unique layer-by-layer construction method but faces obstacles in widespread industrial use due to quality assurance and defect challenges. Integrating machine learning (ML) into AM quality control (QC) systems presents a viable solution, utilizing ML’s ability to autonomously detect patterns and extract important data, reducing the reliance on manual intervention. This study conducts an in-depth literature review to scrutinize the role of ML in augmenting QC mechanisms within extrusion-based AM processes. Our primary objective is to pinpoint ML models that excel in monitoring manufacturing activities and facilitating instantaneous defect corrections via parameter adjustments. Our analysis highlights the efficacy of convolutional neural networks (CNNs) models in defect detection, leveraging camera-based systems for an in-depth examination of printed parts. For 1-D data processing, support vector machines (SVMs) and long short-term memory (LSTM) networks have shown significant application and effectiveness. Furthermore, the study classifies various sensors and defects that can effectively benefit from ML-driven QC approaches. Our findings accentuate the essential role of ML, especially CNNs, in detecting and rectifying production flaws and also detail the synergy between different sensor technologies in creating a comprehensive monitoring framework. By integrating ML with a multisensor approach and employing real-time corrective strategies, such as dynamic parameter adjustments and the use of advanced control systems, this research underscores ML’s transformative potential in elevating AM QC. Thus, our contribution lays the groundwork for harnessing ML technologies to ensure superior quality parts production in AM, paving the way for its broader industrial adoption.
查看更多>>摘要：In this article, we investigate the multiagent robust consensus problem under model uncertainties, where the uncertain matrices and initial values are bounded by prior intervals. Based on the positive system theory, the related upper and lower dynamic systems are constructed to guarantee that the state value remains within a specified range. Subsequently, in accordance with the Lyapunov stability principle, the observation and consensus errors converge to zero, that is, the real states are reconstructed and consensus is achieved. Both local and neighborhood protocols, which are utilized to realize robust consensus, are presented. Notably, the proposed methods increase the design freedom and eliminate the Metzler constraint on the error matrix by introducing two novel parametric matrices. Without loss of generality, the topology in this article is assumed to contain a directed spanning tree, which can be directly degenerated to the undirected graph. Finally, numerical simulations validating the theoretical results are described.
查看更多>>摘要：This article presents reachable set synthesis for a discrete-time Markov jump system (DTMJS) with mode-dependent time-varying delays, subjected to uncertain transition probabilities and actuator faults, based on the ellipsoidal attracting approach. The focus is mainly to reflect more realistic control behaviors for the proposed DTMJS, in which the class of partially asynchronous reliable control (PARC) scheme is designed for the first time under the Markov switching topology. In this regard, the state-feedback and mode-dependent time-varying delayed state-feedback controllers are coupled by employing the Bernoulli variable. Under this framework, the hidden Markov model is formulated, revealing the asynchronism among switching topology, controller, actuators and proposed system in different operational modes. By constructing a double mode-dependent stochastic Lyapunov-Krasovskii functional, the sufficient conditions are derived in terms of linear matrix inequalities, which not only ascertain the stochastic stability of the resultant Markov jump system but also ensure that all reachable states remain within compact ellipsoidal boundaries. Finally, numerical simulations are provided to verify the effectiveness and merits of the presented method.
查看更多>>摘要：Multiagent and multirobot systems (MRS) often rely on direct communication for information sharing. This work explores an alternative approach inspired by eavesdropping mechanisms in nature that involves casual observation of agent interactions to enhance decentralized knowledge dissemination. We achieve this through a novel indirect knowledge transfer through behavior trees (IKT-BT) framework tailored for a behavior-based MRS, encapsulating knowledge and control actions in behavior trees (BT). We present two new BT-based modalities—eavesdrop-update (EU) and eavesdrop-buffer-update (EBU)—incorporating unique eavesdropping strategies and efficient episodic memory management suited for resource-limited swarm robots. We theoretically analyze the IKT-BT framework for an MRS and validate the performance of the proposed modalities through extensive experiments simulating a search and rescue mission. Our results reveal improvements in both global mission performance outcomes and agent-level knowledge dissemination with a reduced need for direct communication.
查看更多>>摘要：Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this article, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise.
查看更多>>摘要：This article presents a high-confidence data-driven safe tracking control design for stochastic linear discrete-time systems. The high-confidence safe reference tracking for an ellipsoidal safe set is first formalized using the concept of probabilistic set-based $\lambda $ -contractivity. A data-driven controller, composed of feedback and feedforward elements, is then designed to enforce the $\lambda $ -contractivity of the safe set. The feedback control gain is learned by 1) providing a data-driven representation of the closed-loop system, which contains a decision variable that affects the control gain and 2) optimizing the decision variable to ensure the $\lambda $ -contractivity. This feedback term can be learned using a data set that is not even rich enough to identify the full system model. A feedforward gain learning algorithm and a data-driven reference governor are provided to satisfy the required conditions on equilibrium terms. It is shown that under certain conditions on the equilibrium terms, the learned tracking controller guarantees the system’s safety and stability with high probability. The reference governor dynamically manipulates the desired reference signal based on the data quality to prevent any breach of safety constraints in a probabilistic manner. It is shown that the output of the reference governor eventually converges to the desired goal states if inside the safe set and high-quality data is available. Therefore, the tracking controller guarantees convergence of the system output to its desired goal while ensuring safety with a high probability. The simulation results on a drone hovering and a test system, comparing the results with the existing literature, confirm that the presented high-confidence data-driven safe tracking control outperforms certainty-equivalent safe control methods.
NETL
NSTL
IEEE