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作者:Xie, Huahui; Dai, Li; Lu, Yuchen; Xia, Yuanqing
作者单位:Beijing Institute of Technology
摘要:This article proposes a novel disturbance rejection model predictive control (DRMPC) framework to improve the robustness of model predictive control (MPC) for a broad class of input-affine nonlinear systems with constraints and state-dependent disturbances. The proposed controller includes two parts-a disturbance compensation input and an optimal MPC control input. The former one is designed to compensate for the matched disturbance actively. This is made possible via a disturbance observer th...
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作者:Hua, Changchun; Ning, Pengju; Li, Kuo
作者单位:Yanshan University
摘要:This article focuses on the problem of prescribed-time control for a class of uncertain nonlinear systems. First, a prescribed-time stability theorem is proposed by following the adaptive technology for the first time. Based on this theorem, a new state feedback control strategy is put forward by using the backstepping method for high-order nonlinear systems with unknown parameters to ensure the prescribed-time convergence. Moreover, the prescribed-time controller is obtained in the form of co...
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作者:Sen, Arijit; Sahoo, Soumya Ranjan; Kothari, Mangal
作者单位:Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur
摘要:During the implementation of a cooperative algorithm, information about the agents' velocity may be unavailable due to the space constraint and availability of sensors. Thus, it gives rise to the design of distributed average tracking (DAT) algorithms without using agents' velocity measurements. These are denoted as velocity-free DAT problems. The existing literature has addressed such problems in the presence of an undirected graph for the reference signals with bounded position, velocity, an...
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作者:Xu, Yong; Yao, Zhaozhan; Lu, Renquan; Ghosh, Bijoy K.
作者单位:Guangdong University of Technology; Texas Tech University System; Texas Tech University
摘要:This technical note studies fixed-time consensus tracking with disturbance rejection for first-order multiagent systems. The communication topology among the leader and followers contains a directed spanning tree. The control input to the leader is time-varying and unknown to the followers, except that its upper bound is known a priori. A novel fixed-time protocol is devised based on discontinuous and nonlinear control. A fixed-time stability analysis for consensus tracking with disturbance re...
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作者:Farina, Francesco; Garulli, Andrea; Giannitrapani, Antonio
作者单位:University of Bologna; GlaxoSmithKline; Glaxosmithkline United Kingdom; University of Siena
摘要:This article addresses distributed estimation problems over asynchronous networks in a set membership framework. The agents in the network asynchronously collect and process measurements, communicate over a possibly time-varying and unbalanced directed graph and may have nonnegligible computation times. Measurements are affected by bounded errors so that they define feasible sets containing the unknown parameters to be estimated. The proposed algorithm requires each agent to compute a weighted...
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作者:Liu, Yanan; Dong, Daoyi; Petersen, Ian R.; Gao, Qing; Ding, Steven X.; Yokoyama, Shota; Yonezawa, Hidehiro
作者单位:University of New South Wales Sydney; Okinawa Institute of Science & Technology Graduate University; University of Duisburg Essen; Australian National University; Beihang University; Beihang University
摘要:Robustness and reliability are two key requirements for developing practical quantum control systems. The purpose of this article is to design a coherent feedback controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop quantum system has both fault tolerance and H-infinity disturbance attenuation performance. This article first extends the physical realization conditions from the time-invariant case to the time-varying case for linear st...
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作者:Lu, Yafei; Gao, Chuanhou; Dochain, Denis
作者单位:Zhejiang University; Universite Catholique Louvain
摘要:This article contributes to extending the validity of Lyapunov function partial differential equations (PDEs) whose solution is conjectured to be able to behave as a Lyapunov function in stability analysis to more mass-action chemical reaction networks. First, we have proved that the Lyapunov function PDEs method is valid in capturing the asymptotic stability of the networks compounded of a complex balanced network and any species-dependent two-species autocatalytic network if some moderate co...
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作者:Ito, Hiroshi
作者单位:Kyushu Institute of Technology
摘要:This article addresses analysis and control of the SIR model of infectious diseases in the framework of input-to-state stability (ISS) with respect to the net flow of susceptible individuals into a region in both disease-free and epidemic situations. The key development is the construction of a continuously differentiable strict Lyapunov function. First, this article clarifies that a continuously differentiable Lyapunov function whose derivative is nonpositive can deduce asymptotic stability o...
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作者:Liu, Wei; Shi, Peng; Wang, Shuoyu
作者单位:Zhejiang Gongshang University; Kochi University Technology; University of Adelaide; Kochi University Technology
摘要:This article is concerned with the distributed Kalman filtering problem for discrete-time linear systems whose measurement information comes from a set of sensor nodes that can communicate with their direct neighbors. Two algorithms for distributed Kalman filtering are proposed, where the first algorithm is based on single-node measurement and the second algorithm is based on neighboring-node measurements. In order to improve the performance, a novel criterion is introduced to the algorithm de...
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作者:Paternain, Santiago; Bazerque, Juan Andres; Ribeiro, Alejandro
作者单位:Rensselaer Polytechnic Institute; Universidad de la Republica, Uruguay; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Pennsylvania
摘要:Reinforcement learning aims to find policies that maximize an expected cumulative reward in Markov decision processes with unknown transition probabilities. Policy gradient (PG)-algorithms use stochastic gradients of the value function to update the policy. A major drawback of PG-algorithms is that they are limited to episodic tasks (multiple finite-horizon trajectories) unless stringent stationarity assumptions are imposed on the trajectories. Hence, they need restarts and cannot be fully imp...