基于积分强化学习的四旋翼无人机鲁棒跟踪  

Robust Tracking of Quadrotor UAVs Based on Integral Reinforcement Learning

作　　者：杨加秀 李新凯 张宏立[1] 王昊 YANG Jiaxiu;LI Xinkai;ZHANG Hongli;WANG Hao(School of Electrical Engineering,Xinjiang University,Urumqi 830017,Xinjiang,China)

机构地区：[1]新疆大学电气工程学院,新疆乌鲁木齐830017

出　　处：《兵工学报》2023年第9期2802-2813,共12页Acta Armamentarii

基　　金：国家自然科学基金项目(51967019、52065064);新疆维吾尔自治区自然科学基金青年项目(2022D01C86、2022D01C89)。

摘　　要：针对系统模型动态不确定和受外部干扰的四旋翼无人机位置轨迹跟踪控制问题,提出一种新的基于积分强化学习的鲁棒轨迹跟踪控制方法。建立四旋翼无人机原系统与参考轨迹的增广系统,将四旋翼无人机的鲁棒轨迹跟踪问题转化为镇定问题。通过使用带有折扣因子的价值函数,将无人机增广系统的鲁棒镇定问题转化为四旋翼无人机的最优控制问题,从而兼顾到四旋翼无人机的跟踪误差和整体性能。基于积分强化学习方法,构建了单网络演员-评论家结构对最优价值函数进行估计,进而实现对四旋翼无人机控制器的在线求解。对四旋翼无人机系统跟踪误差的稳定性及单网络结构权值的收敛性进行了严格的数学证明,仿真结果验证了所设计控制方案的优越性和鲁棒性。A novel robust trajectory tracking control method based on integral reinforcement learning is proposed for the quadrotor UAV position trajectory tracking control with uncertain system model dynamics and external disturbances.Firstly,an augmented system of the original system and reference trajectory of the quadrotor UAV is established to transform the robust trajectory tracking problem of the quadrotor UAV into a sedimentation problem.By using the value function with discount factor,the robust calming problem of the UAV augmented system is transformed into an optimal control problem,taking into account the tracking errors and the overall performance of the quadrotor UAV.Then,based on the integral reinforcement learning method,a single network actor-critic structure is developed to estimate the optimal value function and online solution for the quadrotor UAV controller.Finally,the stability of the quadrotor UAV system tracking errors and the convergence of the single network structure weights are rigorously demonstrated mathematically,and the simulation results verify the superiority and robustness of the proposed control scheme.

关 键 词：四旋翼无人机 鲁棒跟踪控制 积分强化学习 最优控制 不确定性 

分 类 号：TJ301[兵器科学与技术—火炮、自动武器与弹药工程]