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.
In this study, our main objective is to address the issue of sampled-data-based synchronization of complex networks subjected to stochastic scaling attacks using a looped-functional approach. To begin with, the design...
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作者：
Chen, YiweiPan, YuDong, DaoyiZhejiang University
Institute of Cyber-Systems and Control College of Control Science and Engineering Hangzhou310027 China Zhejiang University
State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Hangzhou310027 China University of New South Wales
School of Engineering and Information Technology CanberraACT2600 Australia
States of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modeled. In machine learning, complex multiple multilinear correlations ...
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For back electromotive force-based sensorless control in permanent magnet synchronous motor (PMSM) drives, the conventional phase-locked loop (PLL) exhibits a ±π position estimation error during speed reversal. ...
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This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight...
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This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic *** a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow *** order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is *** experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.
Different sensors may experience degeneracy in specific scenarios, which can lead to failures in multi-sensor fusion optimization. To address the challenges of localization and mapping under such degeneracy conditions...
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Discrete event system(DES)models promote system engineering,including system design,verification,and *** advancement in manufacturing technology has endowed us to fabricate complex industrial ***,the adoption of advan...
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Discrete event system(DES)models promote system engineering,including system design,verification,and *** advancement in manufacturing technology has endowed us to fabricate complex industrial ***,the adoption of advanced modeling methodologies adept at handling complexity and scalability is ***,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the *** this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the *** traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone *** with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new ***,this general modeling approach has wide potential for future complex and intelligent industrial systems.
Output regulation theory is an effective method for achieving accurate time-varying command following and can utilize adaptive internal models to follow arbitrary reference signals generated by an exosystem. However, ...
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This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memri...
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This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memristive neural networks by using event-triggered ***,a switching system is constructed under the event-triggered control ***,by adopting a piece-wise Lyapunov functional,a sufficient condition is established for the exponential synchronization and mixed H_(∞)and passivity ***,an event-triggered controller design scheme is proposed using matrix decoupling ***,the effectiveness of the designed controller is exemplified by a numerical example.
Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks,...
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Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.
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