Yuezu Lv

Prof. Yuezu Lv

College of Mathematics, Southeast University, Nanjing, China
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Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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26 September 2022 in Asian Journal of Control
Verified
Journal Article
Closed access
Guanghui WenGuanghui Wen
Guanghui Wen
PWPeijun Wang
Peijun Wang
YLYuezu Lv
Yuezu Lv
GCGuanrong Chen
Guanrong Chen
JZJialing Zhou
Jialing Zhou
23 April 2021 in IEEE Transactions on Automatic Control
Verified
Journal Article
Closed access
DZDan Zhao
Dan Zhao
YLYuezu Lv
Yuezu Lv
XYXinghuo Yu
Xinghuo Yu
Guanghui WenGuanghui Wen
Guanghui Wen
GCGuanrong Chen
Guanrong Chen
Current resilient consensus algorithms deal with multiagent networks with lower order nodal dynamics where each agent excludes certain extreme values from its neighbors. This does not scale well in general networks. In this article, a new type of resilient consensus algorithm based on distributed attack isolation (DAI-RC) is proposed for general higher order networks. In the DAI-RC algorithm, the evolution of each normal agent can avoid the influence from those neighbors which are isolated as victims of attack. To characterize a feasible communication topology to isolate attacked agents, a notion of graph isolability is proposed based on which a sufficient condition to isolate the attacked agents is presented. Simulations are finally provided to illustrate the effectiveness of the proposed algorithm.
22 March 2021 in IEEE Transactions on Cybernetics
Verified
Journal Article
Closed access
JZJialing Zhou
Jialing Zhou
YLYuezu Lv
Yuezu Lv
Guanghui WenGuanghui Wen
Guanghui Wen
XYXinghuo Yu
Xinghuo Yu
This article aims to establish an appointed-time observer-based framework to efficiently address the resilient consensus control problem of linear multiagent systems with malicious attacks. The local appointed-time state observer is skillfully designed for each agent to estimate the agent’s actual state value at the appointed time, even in the presence of unknown malicious attacks. Based on the state estimation, a new kind of resilient control strategy is proposed, where a virtual system is constructed for each agent to generate an ideal state value such that the consensus of normal agents can be achieved with the exchange of ideal state values among neighboring agents. To specify the consensus trajectory while achieving resilient consensus, the leader–follower resilient consensus is further studied, where the leader is assumed to be a trusted agent with a bounded control input. Compared with the existing results on the resilient consensus, the proposed distributed resilient controller design reduces the requirement on communication connectivity significantly, where the allowable communication graph is only assumed to contain a directed spanning tree. To verify the theoretical analysis, numerical simulations are finally provided.
2 February 2018 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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Journal Article
Closed access
YLYuezu Lv
Yuezu Lv
ZLZhongkui Li
Zhongkui Li
ZDZhisheng Duan
Zhisheng Duan
This paper considers the consensus control problem of heterogeneous linear multiagent systems. A distributed proportional-integral (PI) protocol is presented to ensure consensus of heterogeneous linear multiagent systems with directed communication graphs. Sufficient conditions for the choice of control parameters are derived. For the case that the agents are subject to the external time-varying but bounded disturbances, the proposed distributed PI protocol can assure uniformly ultimate boundedness of the consensus error. The effectiveness of the results is illustrated both theoretically and numerically.
19 June 2019 in IEEE Transactions on Cybernetics
Verified
Journal Article
Closed access
PDPeihu Duan
Peihu Duan
ZDZhisheng Duan
Zhisheng Duan
YLYuezu Lv
Yuezu Lv
GCGuanrong Chen
Guanrong Chen
In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems via sensor networks. A novel robust distributed extended Kalman filter, which can handle norm-bounded uncertainties in both the system model and its Taylor series expansion, is developed with ensured estimation performance. The filter is distributed in the sense that for each sensor, only its own measurements and its neighbors' information are utilized to optimize the upper bound of the estimation error covariance. Besides, a sufficient condition for the proposed algorithm is derived, which is simple and user-friendly since it depends on the property of the original nonlinear system instead of the estimation error covariance calculated at every step. Finally, the simulation results are presented to demonstrate the effectiveness of the filtering algorithm.
6 July 2020 in IEEE Transactions on Cybernetics
Verified
Journal Article
Closed access
QWQishao Wang
Qishao Wang
ZDZhisheng Duan
Zhisheng Duan
YLYuezu Lv
Yuezu Lv
QWQingyun Wang
Qingyun Wang
GCGuanrong Chen
Guanrong Chen
The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent's weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.