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In this paper, two robust consensus problems are considered for a multi-agent system with various disturbances. To achieve the robust consensus, two distributed control schemes for each agent, described by a second-order differential equation, are proposed. With the help of graph theory, the robust consensus stability of the multi-agent system with communication delays is obtained for both fixed and switching interconnection topologies. The results show the leaderless consensus can be achieved with...
This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and -accuracy. Furthermore, a differential privacy...
As embedded microprocessors are applied widerly to multi-agent systems, control scheduling and time-delay problems arose in the case of limited energy and computational ability. It has been shown that the event-triggered actuation strategy is an effective methodology for designing distributed control of multi-agent systems with limited computational resources. In this paper, a tracking control problem of leader-follower multi-agent systems with/without communication delays is formulated and a distributed...
In this paper, a fixed-time safe control problem is investigated for an uncertain high-order nonlinear pure-feedback system with state constraints. A new nonlinear transformation function is firstly proposed to handle both the constrained and unconstrained cases in a unified way. Further, a radial basis function neural network is constructed to approximate the unknown dynamics in the system and a fixed-time dynamic surface control (FDSC) technique is developed to facilitate the fixed-time control...
This paper proposes an online identifier-critic learning framework for event-triggered optimal control of completely unknown nonlinear systems. Unlike classical adaptive dynamic programming (ADP) methods with actor-critic neural networks (NNs), a filter-regression-based approach is developed to reconstruct the unknown system dynamics, and thus avoid the dependence on an accurate system model in the control design loop. Meanwhile, NN adaptive laws are designed for the parameter estimation by using...
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