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基于层级结构的空-地协同预设时间最优容错控制OA北大核心CSTPCD

Hierarchical-based Prescribed-time Optimal Fault-tolerant Control for Air-ground Cooperative System

中文摘要英文摘要

研究了发生执行器故障的无人机-无人车异构编队系统的层级预设时间最优编队控制问题.以保容错性能和收敛速度的优化控制为研究主线,以层级控制、图博弈理论和预设时间控制为技术基础,构建了一种预设时间最优容错控制算法.虚拟层设计了基于一致性跟踪误差和能量消耗的二次型性能指标函数,借助耦合哈密顿-雅克比-贝尔曼(Hanmilton-Jacobi-Bellman,HJB)方程和强化学习求解近似最优控制策略,实现多智能体的同步最优控制和交互纳什均衡.实际控制层基于最优信号并利用滑模控制和自适应技术,设计了预设时间容错跟踪控制器,实现对最优编队轨迹的有限时间跟踪.在保证全局收敛时间完全不依赖于系统的初始状态和控制器参数的同时,也有效实现对执行器故障参数的逼近.最后,通过仿真实验验证了所提控制策略的有效性.

This article investigates the hierarchical structure-based optimal formation control problem of a hetero-geneous formation system of unmanned aerial vehicles and unmanned ground vehicles.This article focuses on the optimization control with fault-tolerant performance and fast convergence speed,and constructs a prescribed-time optimal fault-tolerant control algorithm based on hierarchical control,graphical game theory,and prescribed-time control method.In virtual layer,an quadratic performance index function based on consistency tracking error and energy consumption is designed,and approximate optimal control strategy is obtained by using coupled Hanmilton-Jacobi-Bellman(HJB)equation and reinforcement learning,which achieves synchronous optimal control and inter-active Nash equilibrium of multiagent systems.In actual control layer,a prescribed-time fault-tolerant tracking con-troller is designed based on the optimal signal,sliding-mode and adaptive technologies,which realizes the finite-time tracking of the optimal formation trajectory.The proposed method ensures that the global convergence time is com-pletely independent of the initial states of the system and controller parameters,while also effectively approximat-ing the actuator fault parameters.Finally,the effectiveness of the constructed control strategy is verified through simulation experiment.

成旺磊;张柯;姜斌

南京航空航天大学自动化学院 南京 211106

空-地协同执行器故障预设时间编队图博弈最优控制

Air-ground cooperationactuator faultsprescribed-time formationgraphical gameoptimal control

《自动化学报》 2024 (008)

1589-1600 / 12

国家自然科学基金(62020106003,62173180,62233009),江苏省自然科学基金(BK20222012),高等学校学科创新引智计划(B20007),中央高校基本科研业务费(NC2022003,NE2022002),江苏高校"青蓝工程",国家资助博士后研究人员计划(GZB20240974)资助Supported by National Natural Science Foundation of China(62020106003,62173180,62233009),Natural Science Foundation of Jiangsu Province of China(BK20222012),111 Project of the Programme of Introducing Talents of Discipline to Universities of China(B20007),the Fundamental Research Funds for the Cent-ral Universities(NC2022003,NE2022002),Qing Lan Project of Jiangsu Province of China,and Postdoctoral Fellowship Pro-gram of CPSF(GZB20240974)

10.16383/j.aas.c230699

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