supported by the National Natural Science Foundation of China[grant numbers 61503118,62006135].
Event-triggered control(ETC)of multi-agent systems(MASs)has been extensively investigated due to its advantages in conserving communication resources and reducing control frequency.This paper provides a systematic rev...
supported in part by the National Natural Science Foundation of China under Grants 62173079 and U1808205;the Science and Technology Program of Gansu Province under Grant 21ZD4GA028.
This paper addresses the ultimate boundedness control problem for a class of networked nonlinear systems with the round-robin(RR)protocol and uniform quantisation.The communication between sensor nodes and the control...
supported in part by the Funds of National Science of China[grant numbers 62373176,61973146,and 62373206];in part by the Distinguished Young Scientific Research Talents Plan in Liaoning Province[grant number XLYC1907077];in part by the Applied Basic Research Program in Liaoning Province[grant number 2022JH2/101300276];in part by the Taishan Scholar Project of Shandong Province of China[grant number tsqn201909097].
This paper concentrates on the event-triggered adaptive asymptotic tracking control problem for a class of nonlinear systems.By using the neural network,the unknown nonlinearities existing in the systems are skilfully...
supported in part by the US Office of Naval Research under grants N00014-20-1-2858,N00014-22-1-2001,and N00014-23-1-2124.
In this paper,we propose an observer-based algorithm for balancing the state-of-charge(SoC)among battery units in a battery energy storage system(BESS).The dynamical behaviour of a battery unit is approximated by an e...
supported by the National Natural Science Foundation of China under grant numbers 72101168,71571123;China Postdoctoral Science Foundation under grant number 2021M692259.
New opportunities and challenges for information representation and processing are brought about by the rapid development of artificial intelligence.This study offers a new decision-making method that decreases inform...
supported by National Natural Science Foundation of China:[Grant Number 61763028];“Innovation Star”Project for Outstanding Graduate Students in Gansu Province:[Grant Number 2021CXZX-515].
Multi-agent Reinforcement Learning(MARL)has become one of the best methods in Adaptive Traffic Signal Control(ATSC).Traffic flow is a very regular traffic volume,which is highly critical to signal control policy.Howev...
In this article,the authors set up an optimal control of neutral stochastic integro-differential equations(NSIDEs)driven by fractional Brownian motion(fBm)in a Hilbert space by using Grimmer resolvent operators.Suffic...
supported in part by the National Natural Science Foundation of China under Grant 62233006,62173221;in part by Shanghai Rising-Star program under Grant 20QA1404000.
Recent application studies of deep reinforcement learning(DRL)in power electronic systems have successfully demonstrated its superiority over conventional model-based control design methods,stemming from its adaption ...
supported by the Science and Engineering Research Board(SERB),DST,India[grant number SRG/2022/000892].
This paper proposes a model-based reference tracking scheme for stable,MIMO,nonlinear processes.A Joint Unscented Kalman Filtering technique is exploited here to develop a stochastic model of the physical process via ...
This paper adopts an M/G/1 retrial queueing system with imperfect coverage and reboot delay.When the system detects an arrival,it will immediately process the arrival.On the other hand,if the arrivals are not detected...