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作 者:王伟[1,2] 杨婧[1,2] 南宇翔 李俊辉 王雨辰 WANG Wei;YANG Jing;NAN Yuxiang;LI Junhui;WANG Yuchen(School of Aerospace Engineering,Beijing Institute of Technology,Beijing Institute of Technology,Beijing 100081,China;China-UAE Belt and Road Joint Laboratory on Intelligent Unmanned System,Beijing Institute of Technology,Beijing 100081,China;China North Industries Corporation,Beijing 100053,China)
机构地区:[1]北京理工大学宇航学院,北京100081 [2]北京理工大学中国-阿联酋智能无人系统“一带一路”联合实验室,北京100081 [3]中国北方工业有限公司,北京100053
出 处:《兵工学报》2024年第11期3841-3855,共15页Acta Armamentarii
基 金:国家自然科学基金项目(52272358、62103052)。
摘 要:旋转弹在飞行过程中受多种干扰的影响,包括跨域飞行气动参数剧烈变化引起的模型不确定性以及飞行过程中受到的外部扰动。为了解决高动态飞行环境中双通道旋转弹的鲁棒控制问题,基于轨迹线性化控制方法,设计伪逆反馈控制器。采用径向基函数神经网络,设计自适应前馈补偿控制器,有效实现对模型不确定性的精确逼近。将神经网络逼近误差和外部扰动处理为总扰动,并基于固定时间稳定理论设计一种自适应扰动观测器,实现对总扰动的精确估计及补偿。通过Lyapunov理论,严格证明了闭环系统的最终一致有界性。通过数值仿真验证了所设计方法的有效性。The spinning projectiles are influenced by various disturbances during flight,includingthe model uncertainties due to the drastic variations in aerodynamic parameters during cross-domain flight,as well as the external perturbations caused by external forces and moments.The research aims to address the robust control challenges of dual-channel spinning projectiles in high-dynamic flight environments.A pseudo-inverse feedback controller is designed based on the trajectory linearization control method,and an adaptive feedforward compensation controller is developed using radial basis function neural networks to accurately approximate the model uncertainties.Finally,an adaptive disturbance observer is designedby treating the neural network approximation errors and external disturbances as total disturbance based on the fixed-time stability theory,which is used to accurately estimate and compensate for total disturbance.The ultimate uniform boundness(UUB)of the closed-loop system is rigorously proven through Lyapunov theory.The effectiveness of the proposed methodology is illustrated through numerical simulations.
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