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Research on Lyapunov-based Predictive Path Following Control of AGV Based on Time Constraint
Shaobo Zhang,Qinxiang Xia,Gangfeng Xiao,Sizhu Cheng,Mingxing Chen 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.12
Automated Guided Vehicles (AGV) usually takes on the responsibility of material transmission in industrial applications with a wireless computer-controlled differential driving. A Lyapunov-based model predictive control method is proposed for the AGV path following problem with considering the time constraint. The time constraint problem of path following is introduced for industrial scheduling fields, and the reference path is discretized and virtually constructed by the quadratic programming method based on the double integrator technique. The stabilization point control theorem is migrated to solve the path following control, and adopted to track the generated discretized setting reference path points by using the model predictive control method. The predictive following control method is constrained by the Lyapunov function, which ensures the stability of the control of AGV motion. The controller is constrained by Lyapunov stability criteria to demonstrate the local and global convergence of the control system. The comparative results of numerical simulation experiments show that the proven method is effective with better smoothness and stability.
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Li Shaobo,Li Chuanjiang,Zhang Ansi,Yang Lei,Zio Enrico,Pecht Michael,Gryllias Konstantinos 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.4
As fixed-wing unmanned aerial vehicles (FW-UAVs) are used for diverse civil and scientific missions, failure incidents are on the rise. Recent rapid developments in deep learning (DL) techniques offer advanced solutions for fault diagnosis of unmanned aerial vehicles. However, most existing DL-based diagnostic models only perform well when trained on massive amounts of labeled data, which are challenging to collect due to the complexity of the FW-UAVs systems and service environments. To address these issues, this paper presents a novel framework, Siamese hybrid neural network (SHNN), to achieve few-shot fault diagnosis of FW-UAVs in an intelligent manner. “State map” strategy is firstly proposed to transform raw flight data into similar and dissimilar sample pairs as input. The proposed SHNN framework consists of two identical networks that share weights with each other, and each subnetwork is designed with a hybrid one-dimensional conventional neural network and long short-term memory model as feature encoder, whose generated feature embedding is used to measure the similarity of input pairs via a distance function in the metric space. In comprehensive experiments on a real flight dataset of an FW-UAV, the SHNN framework achieves competitive results compared to other models, demonstrating its effectiveness in both binary and multi-class few-shot fault diagnosis.
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( Xianxian Wang ),( Shaobo Lv ),( Xing Wang ),( Zhongshan Zhang ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.4
Resource allocation in device-to-device (D2D) aided cellular systems, in which the proximity users are allowed to communicate directly with each other without relying on the intervention of base stations (BSs), is investigated in this paper. A new uplink resource allocation policy is proposed by exploiting the relationship between D2D-access probability and channel gain among variant devices, such as cellular user equipments (CUEs), D2D user equipments (DUEs) and BSs, etc., under the constraints of their minimum signal to interference-plus-noise ratio (SINR) requirements. Furthermore, the proposed resource-allocation problem can be formulated as the cost function of “maximizing the number of simultaneously activated D2D pairs subject to the SINR constraints at both CUEs and DUEs”. Numerical results relying on system-level simulations show that the proposed scheme is capable of substantially improving both the D2D-access probability and the network throughput without sacrificing the performance of conventional CUEs.
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Huili Wang,Shaobo Zhou,Yan Zhang,Sifeng Qin 한국강구조학회 2023 International Journal of Steel Structures Vol.23 No.6
Tensile-plate anchorage (TPA) is widely used in steel cable-stayed bridges. However, due to the complex geometry of the anchorage, the stress concentration area will appear under the action of the dynamic load, leading to fatigue failure. In this paper, taking a cable-stayed bridge as an example, the theoretical analysis and experimental investigation of the fatigue performance of TPA are performed. At first, a full-bridge finite element model was set up to obtain the severest cable force and cable force amplitude under fatigue load. Then the fatigue test of 1:1.5 specimen was carried out. The maximum principal stress of TPA was 32.24 MPa and the maximum deformation was 1.6 mm. During the loading process, TPA was in elastic working state. After 2 million fatigue load cycles, no cracks were found. At last, the fatigue performance of TPA was analyzed with the theory of critical distances (TCD). The results of the theoretical analysis and fatigue test show that the fatigue performance of TPA could satisfy the use requirement.
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Academic Management and Analysis Method of MOOC
Fei Lang,Siwen Zhang,Shaobo Li,Guanglu Sun 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
MOOC (Massive Open Online Courses), is a new mode of distance education courses based on the Internet technology. To solve problems in MOOC, such as lacks of management, supervision and comprehensive evaluation etc., this paper aims to study MOOC academic management and analysis method in contexts of colleges and universities. To supervise and trace students’ learning process, an academic management system of MOOC was constructed. To assess and predict students’ learning trends and achievements, an academic analysis model was established based on learning and interaction data analysis. Experimental results prove the above method insures expected learning effects when applying MOOC in colleges and universities.
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