Integrated Framework for Cooperative Mutual Estimation and Control in Multi-agent Systems
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摘要: 尽管多智能体系统协同控制已有广泛研究, 现有分布式控制算法在个体传感器受损情况下仍存在性能下降问题. 提出一种协同互估计与控制一体化设计新框架, 通过充分利用个体传感器对其他智能体的测量信息, 提升多智能体系统协同控制的弹性能力. 首先, 对整个多智能体系统构建分布式传感网络模型. 其次, 基于既定的协同控制任务, 建立个体对整体控制输入的预测估计; 进一步设计全局整体测量输出的分布式一致性追踪估计器. 然后, 利用整体控制输入预测和整体测量输出追踪, 设计局部观测器实现整体状态估计. 此外, 将所提的一体化设计框架应用于线性多智能体系统协同一致性控制问题, 提出反馈增益的联合设计方法, 从理论上验证了所提框架的有效性. 仿真结果进一步表明, 该框架能够适用于多智能体系统部分传感器受损情形下的协同控制任务. 最后, 探讨协同互估计与控制一体化框架的未来研究方向.Abstract: Although cooperative control of multi-agent systems has been extensively studied, existing distributed control algorithms still suffer the problem of performance degradation when individual sensor failure occurs. This paper proposes a novel integrated design framework for cooperative mutual estimation and control, which enhances the resilience of multi-agent system cooperative control by fully leveraging the measurement information of other agents from individual sensors. Firstly, a distributed sensing network model is constructed for the overall multi-agent systems. Secondly, based on the predefined cooperative control task, each individual establishes a predictive estimate of the global control input; A distributed consensus-based tracking estimator is further designed for reconstructing the global measurement output. Then, a local observer is designed to estimate the overall state using both the global control input prediction and the global measurement output tracking. Furthermore, the proposed integrated design framework is applied to the cooperative consensus control problem of linear multi-agent systems, and a joint design method for the feedback gains is introduced. Theoretical analysis verifies the effectiveness of the proposed framework. Simulation results further demonstrate that the framework is capable of performing cooperative control tasks of multi-agent systems even in the presence of partial sensor failure. Finally, potential directions for future research on integrated framework of cooperative mutual estimation and control are discussed.
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图 1 输入预测–输出追踪的分布式互估计与控制 一体化框架
Fig. 1 Integrated framework of distributed mutual estimation and control with input prediction-output tracking
图 3 多智能体系统输出追踪误差$\tilde{y}_{i4}$收敛
Fig. 3 Convergence of the output tracking error$\tilde{y}_{i4}$in multi-agent systems
图 4 多智能体系统状态估计误差$\tilde{x}_{i1}$收敛
Fig. 4 Convergence of the state estimation error$\tilde{x}_{i1}$in multi-agent systems
图 5 多智能体系统状态分量$x_{i1}$和$x_{i2}$的轨迹趋于一致
Fig. 5 Trajectories of the state components${x}_{i1}$and${x}_{i2}$in multi-agent systems converge to consensus
图 6 智能体5传感器失效情形下多智能体系统输出追踪误差$\tilde{y}_{i4}$收敛
Fig. 6 Convergence of the output tracking error$\tilde{y}_{i4}$in multi-agent systems under sensor failure of agent 5
图 7 智能体5传感器失效情形下多智能体系统状态估计误差$\tilde{x}_{i1}$收敛
Fig. 7 Convergence of the state estimation error$\tilde{x}_{i1}$in multi-agent systems under sensor failure of agent 5
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