多智能体系统事件触发固定时间最优一致性
Event-Based Fixed-Time Optimal Consensus of Multi-agent Systems
甘勤涛 1李瑞鸿 1茹怡珊1
作者信息
- 1. 陆军工程大学 石家庄校区,河北 石家庄 050003
- 折叠
摘要
针对多智能体系统(multi-agent systems,MASs)固定时间最优领导-跟随一致性问题,基于性能优化目标,设计了一种基于事件触发机制的最优控制策略,兼顾固定时间最优一致性控制目标和有限的系统通信计算资源.为了近似求解 Hamilton-Jacobi-Bellman(HJB)方程获得最优值函数的表达式,提出一种仅包含Critic神经网络结构的自适应动态规划(adaptive dynamic programming,ADP)在线学习算法,结合梯度下降法和经验重放方法,利用历史记录数据和当前数据更新神经网络权重向量近似最优值函数及其梯度.采用基于无人地面车辆的无人集群系统验证了该方法的可行性.
Abstract
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.
关键词
多智能体系统/最优控制/固定时间一致性/自适应动态规划/事件触发机制Key words
multi-agent systems(MASs)/optimal control/fixed-time consensus/adaptive dynamic programming(ADP)/event-triggered mechanism引用本文复制引用
基金项目
国家自然科学基金(61305076)
国家自然科学基金(12171416)
出版年
2024
NETL
NSTL
维普
万方数据