The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the de...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
作者:
Yue, HaoXu, YakunHu, HesuanWu, WeiminLi, Lingxi
College of Computer Science and Technology Qingdao266580 China Xidian University
School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University
School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University
State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University
Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University
Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States
This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop ...
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Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has seve...
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Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision *** conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under *** this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put ***,the loop gain is extracted out by adding a gain-monitoring *** demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation *** experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 *** technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.
In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communicatio...
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Hypergraphs generalize graphs in such a way that edges may connect any number of nodes. If all edges are adjacent to the same number of nodes, the hypergraph is called uniform. Thus, a graph is a 2-uniform hypergraph....
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Recently, massive services from multiple domains are available on the network with the rapid development of Mobile Edge Computing (MEC) technology. These various services construct a service network through services’...
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Recently, massive services from multiple domains are available on the network with the rapid development of Mobile Edge Computing (MEC) technology. These various services construct a service network through services’ collaboration, which can provide users with rich resources at anytime and anywhere. However, in MEC, due to the proliferation of services, as well as the dynamics of Quality of Service (QoS), it becomes more and more difficult for users to discover the premium services effectively, which seriously impacts users’ satisfaction and the utilization of resources. Therefore, how to proactively perceive the emergence of premium services in the service networks has become an challenge. To tackle this issue, we propose a method for perception of the premium services based on the evolution of hyper-service network (named as PPSHSN). First, based on the hypergraph theory, we construct a hyper-service network with the optimal connection method. Then, we develop the evolution mechanism of the hyper-service network and optimize the hyper-service network with the hyperdegree premium value evolution algorithm, thus to conduct the evolution of the service networks. Next, we devise the evaluation indexes based on the topological characteristics of hypergraphs, which are updated according to the evolution of the hyper-service network. Finally, we complete perception of premium services based on the evolution results of the hyper-service network. The effectiveness and feasibility of our proposed method are verified through extensive simulation experiments.
This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, ...
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In this research, a fuzzy adaptive PD control approach is introduced for managing the coupled indoor temperature and humidity system. Initially, the mathematical framework of indoor temperature and humidity is analyze...
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Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and nu...
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Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.
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