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Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances 被引量:16
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作者 Qinglai Wei Xin Wang +1 位作者 xiangnan zhong Naiqi Wu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第2期423-431,共9页
This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion.Its main contribution is that a control scheme is designed to achieve the ... This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion.Its main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic noise.In traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent agents.By introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local interaction.Through the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is analyzed.Two simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method. 展开更多
关键词 Consensus control directed topology external disturbance multi-agent(MA)systems
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Advanced Policy Learning Near-Optimal Regulation 被引量:3
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作者 Ding Wang xiangnan zhong 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第3期743-749,共7页
Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynam... Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost. 展开更多
关键词 Adaptive CRITIC algorithm learning control NEURAL APPROXIMATION nonaffine DYNAMICS optimal REGULATION
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