Event-Based Fixed-Time Optimal Consensus of Multi-agent Systems
This paper focuses on the optimal fixed-time leader-following consensus of multi-agent sys-tems(MASs).Firstly,based on the goal of performance optimization,an event-triggered optimal control strate-gy is designed,which takes into account the fixed-time optimal consistency control objectives and limited system communication and computation resources.Secondly,an adaptive dynamic programming(ADP)online learning algorithm is proposed to approximately solve the solution of Hamilton-Jacobi-Bellman(HJB)equation to obtain the expression of the optimal value function,where the Critic neural network structure is only uti-lized.Thirdly,combined with the gradient descent method and experience replay approach,the weight vec-tor is updated to approximate the cost function and its gradient at the triggering instants by employing the historical record and current data.Finally,the unmanned swarm systems composed of unmanned ground vehicles(UGVs)are utilized to verify the feasibility of the proposed method.
NETL
NSTL
万方数据
维普
