多智能体系统输入约束下的一致性与轨迹规划研究

闫敬; 关新平; 罗小元; 杨晛

doi:10.3724/SP.J.1004.2012.01074

多智能体系统输入约束下的一致性与轨迹规划研究

doi: 10.3724/SP.J.1004.2012.01074

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出版历程
- 收稿日期: 2010-05-18
- 修回日期: 2011-05-28
- 刊出日期: 2012-07-20

Consensus and Trajectory Planning with Input Constraints for Multi-agent Systems

摘要

摘要: 针对多智能体系统提出了一种分布式预测控制方法. 首先, 研究了有输入约束下的一致性问题. 其次, 对环境中有障碍物的多智能体轨迹规划进行了研究, 其中只有当障碍物进入智能体有限感知区域内时, 障碍物状态信息才能被获取. 基于预测控制方法, 设计了一种分布式控制算法来解决上面两个问题. 构造一个与每个智能体动力学相交互的代价函数, 设计相应最优控制问题, 从而实现优化控制算法. 智能体间交互信息是其邻居在上一时刻的最优控制状态. 系统稳定性可以通过构造代价函数中的一个终点状态控制器与最优控制问题中的一个终点状态区域来保证. 仿真研究表明所提方法的有效性.
- 多智能系统 /
- 一致性 /
- 轨迹规划 /
- 预测控制
Abstract: This paper presents a distributed receding horizon approach for multi-agent systems. First, the consensus problem with input constraints is considered. Second, the trajectory planning of multi-agent systems (MASs) evolving in environment with obstacles is studied, in which the information of obstacles can be obtained online once the obstacles are sensed by the agents within a limited sensing range. Based on receding horizon approach, a distributed control algorithm is developed to solve the two problems simultaneously. The algorithm can be given by solving the optimal control problem, in which the cost function is coupled with the dynamics of each interacting agent. The exchanged information between agents is the previous optimal control states of their neighbors at each update step. The system stability can be guaranteed with a terminal-state penalty in the cost function and a terminal-state region in the optimal control problem. Simulation studies are provided to verify the effectiveness of the proposed approach.
- Multi-agent systems (MASs) /
- consensus /
- trajectory planning /
- receding horizon approach

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