基于RFNN的大载重无人机路径跟踪控制  

Path tracking control of heavy-load UAV based on RFNN

作　　者：吴建勇 李辉 林和 潘科宇 WU Jianyong;LI Hui;LIN He;PAN Keyu(Lishui Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Lishui 323000,China)

机构地区：[1]国网浙江省电力有限公司丽水供电公司,丽水323000

出　　处：《航天控制》2025年第2期33-39,共7页Aerospace Control

摘　　要：针对受模型不确定和外部未知扰动影响的大载重四旋翼无人机路径跟踪控制问题,提出一种基于随机前馈神经网络的自适应滑模控制方法。该方法通过将大载重四旋翼无人机动力学系统飞行过程中遭受的模型不确定和外部未知扰动定义为集总干扰项,借助于随机前馈神经网络对各个通道中的集总干扰项进行自适应估计和自适应滑模控制器的补偿,依据李雅普诺夫稳定性分析方法给出了大载重四旋翼无人机路径跟踪误差收敛的严格证明,仿真结果充分验证了所提控制方法的有效性。An adaptive sliding mode control strategy based on the randomized feedforward neural network is proposed for the path tracking control problem of heavy-load quadrotor unmanned aerial vehicle(UAV)affected by model uncertainties and external unknown disturbances.The dynamics system of heavy-load quadrotor UAV is experienced by the model uncertainties and external unknown disturbances during flight which are defined as the lumped disturbance term.By taking advantage of randomized feed-forward neural network,the lumped disturbance term in each channel is adaptively estimated and then used to compensate the adaptive sliding mode controller.Based on the Lyapunov stability analysis method,a rigorous proof of the convergence of path tracking errors for heavy-duty quadrotor UAV is presented.The effectiveness of the proposed control method is fully verified by the simulation results.

关 键 词：大载重四旋翼无人机 随机前馈神经网络 滑模控制 路径跟踪控制 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]