摘要
针对存在外界干扰和电机迟滞效应的四旋翼无人机系统,提出了一种基于Nussbaum函数的预设性能神经网络自适应动态面轨迹跟踪控制方法。引入Bouc-Wen迟滞模型刻画电机响应的非线性滞后特性,借助Nussbaum函数处理未知控制增益函数问题,保证控制系统可解性。采用动态面方法降低控制器复杂度,缓解传统反步法的“计算膨胀”问题。针对系统不确定性与外界干扰,采用径向基函数神经网络进行在线逼近,增强系统自适应能力。通过预设性能函数约束轨迹跟踪误差的瞬态与稳态表现,确保其收敛至预设范围。基于Lyapunov稳定性理论证明闭环系统所有信号一致最终有界,跟踪误差收敛至预设性能区域。依托MATLAB软件完成位姿控制仿真,结果验证了该方法在四旋翼轨迹跟踪控制中的有效性。
For quadrotor UAV systems subject to external disturbances and motor hysteresis effects,this paper proposes a preset performance neural network adaptive dynamic surface trajectory tracking control method based on Nussbaum function.The Bouc-Wen hysteresis model is introduced to characterize the nonlinear lag characteristics in motor response,while the Nussbaum function addresses the unknown control gain function problem,ensuring the solvability of the control system.The dynamic surface approach is employed to reduce controller complexity and mitigate the“explosion of complexity”issue inherent in traditional backstepping methods.To handle system uncertainties and external disturbances,a radial basis function(RBF)neural network performs online approximation,enhancing the system’s adaptive capability.A preset performance function constrains both transient and steady-state behavior of trajectory tracking errors,ensuring convergence within a predefined range.Based on Lyapunov stability theory,all signals in the closed-loop system are proven to be uniformly ultimately bounded,with tracking errors converging to the preset performance region.Trajectory tracking simulation was conducted using MATLAB software,with results validating the effectiveness of this method in quadcopter trajectory tracking control.
作者
彭易烩
徐雪松
张义笑
葛泉波
PENG Yihui;XU Xuesong;ZHANG Yixiao;GE Quanbo(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China;State Key Laboratory of Environment Characteristics and Effects for Near-space,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Provincial University Key laboratory of Big Data Analysis and Intelligent Systems,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《河南科技大学学报(自然科学版)》
2026年第2期45-57,M0005,共14页
Journal of Henan University of Science And Technology(Natural Science)
基金
国家自然科学基金重点项目(62033010)
江苏省青蓝工程项目(R2023Q07)。
