Fully distributed time-varying formation tracking control for multiple quadrotor vehicles via finite-time convergent extended state observer

This paper investigates a time-varying anti-disturbance formation problem for a group of quadrotor aircrafts with time-varying uncertainties and a directed interaction topology. A novel Finite-Time Convergent Extended State Observer (FTCESO) based fully-distributed formation control scheme is proposed to enhance the disturbance rejection and the formation tracking performances for networked quadrotors. By adopting the hierarchical control strategy, the multi-quadrotor system is separated into two subsystems: the outer-loop cooperative subsystem and the inner-loop attitude subsystem. In the outer-loop subsystem, with the estimation of disturbing forces and uncertain dynamics from FTCESOs, an adaptive consensus theory based cooperative controller is exploited to ensure the multiple quadrotors form and maintain a time-varying pattern relying only on the positions of the neighboring aircrafts. In the inner-loop subsystem, the desired attitude generated by the cooperative control law is stably tracked under a FTCESO-based attitude controller in a finite time. Based on a detailed algorithm to specify the cooperative control protocol, the feasibility condition to achieve the time-varying anti-disturbance formation tracking is derived and the rigorous analysis of the whole closed-loop multi-quadrotor system is given. Some numerical examples are conducted to intuitively demonstrate the effectiveness and the improvements of the proposed control framework.     

基于多智能体的多机型协同作战决策系统模型研究

多机型协同作战决策系统开发是一项很复杂的工程,各组成要素之间的活动很难进行定量分析和描述,战场情况也复杂多变,具有不可预见性和不可再现性。文中基于目前多机协同作战仿真的发展现状,运用多智能体(Multi-agent)技术,提出了多机型协同作战复杂系统分析与设计决策仿真模型;介绍了运用群建模和开发工具对基于Multi-agent的多机型协同作战决策系统仿真的实现。     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.     

多智能体系统旋转一致控制

研究了多智能体系统的旋转一致控制问题。在存在时滞条件下提出了一个新的控制协议,利用Lyapunov 理论,分析了闭环系统的稳定性,给出了通信拓扑结构不断切换条件下,系统实现旋转一致的条件。最后,通过仿真验证了所得的理论结果。
     

基于外包合同网的自治电磁探测卫星群任务规划

针对具有星间通信能力的自治电磁探测卫星群任务及载荷特点,建立了基于MAS (Multi\|Agent System)的电磁探测卫星群自主规划模型,提出了基于外包合同网的电磁探 测卫星群自主规划算法。根据模拟的数据进行仿真实验及分析,该方法能有效解决电磁 探测卫星群自主任务规划问题。
     

复杂不确定性下多无人机的抗扰时变编队控制

针对存在不确定非线性动态和外部时变干扰的多无人机系统的时变编队问题，提出了基于扩张状态观测器(ESO)的抗扰编队控制方法。首先建立了分布式ESO来估计多无人机系统的不确定性，基于ESO的输出提出了抗扰编队控制律，并提出一套算法来对控制律进行参数选定。然后，通过分析得到基于该控制律下，多无人机系统实现抗扰时变编队所需要的充要条件，并最终严格证明了在满足编队充要条件和基于提出的控制律下，多无人机系统可以稳定实现抗扰时变编队。最后仿真结果表明理论方法的有效性。     

A multi-agent based intelligent configuration method for aircraft fleet maintenance personnel

A multi-agent based fleet maintenance personnel configuration method is proposed to solve the mission oriented aircraft fleet maintenance personnel configuration problem. The mainte- nance process of an aircraft fleet is analyzed first. In the process each aircraft contains multiple parts, and different parts are repaired by personnel with different majors and levels. The factors and their relationship involved in the process of maintenance are analyzed and discussed. Then the whole maintenance process is described as a 3-layer multi-agent system （MAS） model. A com- munication and reasoning strategy among the agents is put forward. A fleet maintenance personnel configuration algorithm is proposed based on contract net protocol （CNP）. Finally, a fleet of 10 aircraft is studied for verification purposes. A mission type with 3 waves of continuous dispatch is imaged. Compared with the traditional methods that can just provide configuration results, the proposed method can provide optimal maintenance strategies as well.     

目标跟踪多智能体一致控制

研究了二阶多智能体系统跟踪一致问题。针对相对速度信息未知的网络,分析了有时滞和无时滞情况下网络的稳定性,给出了系统能够实现跟踪一致的条件。最后,通过仿真表明所提协议比现有协议性能更好。     

复杂约束下的编队姿态有限时间协同控制方法

综合考虑无角速度量测、外部扰动和系统参数不确定性等约束条件的影响，研究航天器编队姿态有限时间协同控制问题。首先建立航天器相对姿态协同控制模型，利用扩张观测器实现对系统姿态角速度及耦合扰动的估计；在此基础上提出了一种有限时间滑模姿态协同控制律；通过构造合适的Lyapunov函数证明了系统相对姿态误差能够在有限时间内收敛到有界域内；将该结果推广到存在饱和输入情形下，并设计了相应的有限时间滑模姿态协同控制律。仿真结果校验了算法的有效性。     

航空结构件智能化加工设备的发展方向

随着工业4.0时代的到来,航空结构件的加工将逐步从数字化时代进入智能化时代,其主要特点是机床软、硬件设备等多智能体系统(Multi-Agent System,MAS)的集成及应用,核心技术在于机床对加工程序的智能化识别,在此基础上实现智能化装夹与基准识别、加工系统实时监控、加工参数的智能化匹配、加工系统智能预警。