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作 者:刘晓栋 吴庆宪[1] 陈谋[1] 邵书义[1] LIU Xiaodong;WU Qingxian;CHEN Mou;SHAO Shuyi(College of Automation Engineering,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China)
机构地区:[1]南京航空航天大学自动化学院,南京211106
出 处:《南京航空航天大学学报》2021年第6期918-927,共10页Journal of Nanjing University of Aeronautics & Astronautics
基 金:江苏省重点研发计划(社会发展)(BE2020704)资助项目;江苏省“333高层次人才培养工程”(BRA2019051)资助项目。
摘 要:针对无人机绕速度矢量轴滚转机动下状态易越界的问题,研究了一种自适应滑模边界保护控制方法。首先,基于二分法思想改进可达平衡集并引入指令约束方法,进而实现在线边界解算与约束指令生成。其次,为抑制不确定性和外部未知干扰对闭环系统的不利影响,利用径向基神经网络逼近系统不确定性并设计非线性二阶干扰观测器估计复合干扰,进一步设计自适应滑模边界保护控制器以实现无人机在安全边界内的姿态跟踪。最后,通过数值仿真验证了自适应滑模边界保护控制方法的有效性。Aiming at the problem that unmanned aerial vehicles(UAVs)are easy to overstep the boundary when rolling around the velocity vector axis,an adaptive sliding mode-based boundary protection control scheme is studied in this paper. Firstly,the attainable equilibrium set is improved based on dichotomy,and the command limitation method is introduced to realize online boundary solution and limited command generation. Then,to restrain the adverse impacts of uncertainties and disturbances to the closed-loop-system,a radial basis function(RBF)neural network is used to approximate the system uncertainties,and a nonlinear second-order disturbance observer is used to estimate the complex disturbances. Accordingly,an adaptive sliding mode-based boundary protection control law is designed to ensure the UAV’s safety and simultaneously realize the attitude tracking. Finally,the effectiveness of the adaptive sliding mode-based boundary protection control method is verified by numerical simulation.
关 键 词:大机动无人机 可达平衡集 指令约束 径向基神经网络 干扰观测器
分 类 号:V249.1[航空宇航科学与技术—飞行器设计]
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