摘要
无人机在不确定气流扰动下容易出现飞行轨迹偏移,通过飞行轨迹跟踪控制可以提高无人机飞行中对不确定气流扰动的抗干扰性和稳健性。传统方法采用滑膜同步协调控制方法,飞行参数的自整定性能受到扰动气流的误差漂移影响较大,控制性能不好。提出一种基于多传感信息的自适应融合跟踪误差补偿的不确定气流扰动下无人机飞行轨迹跟踪控制算法。对无人机飞行轨迹跟踪控制对象,在加入不确定气流下进行无人机飞行动力学模型构建,用多个连续时滞非光滑系统对无人机的定常运动进行运动平衡分解,进行多传感信息的自适应融合跟踪误差补偿,对无人机飞行轨迹的多传感器阵列姿态参量全部量化信息进行自适应参量估计,采用反馈控制,实现飞行轨迹的自适应跟踪控制算法改进。仿真结果表明,该控制算法进行无人机飞行轨迹跟踪控制的精度较高,品质较好,飞行轨迹的预测误差快速收敛到零,提高了飞行稳定性和抗扰动性。
There is flight trajectory deviation for Unmanned Aerial Vehicle （UAV） in the uncertain air flow disturbance of which the re- sistance and the robustness are improved by the flight trajectory tracking control. In traditional methods, the self tuning performance of the flight parameters is influenced by the error drift of the disturbed air flow, with poor control performance. Therefore, an adaptive fusion tracking error compensation method based on multi sensor information is proposed to control the flight trajectory tracking of UAV under uncertain disturbance. For the control object of UAV flight trajectory tracking, a UAV flight dynamics model is constructed in adding un- certain flow, with a continuous delay non smooth system of UAV motion exercise balance decomposition, adaptive fusion tracking error compensation of multi sensor information, adaptive parameter estimation of all quantitative information of multi-sensor array pose param- eters for UAV flight trajectory. Using feedback control, the adaptive trajectory tracking control algorithm is improved. The simulation shows that the control algorithm owns higher precision and better quality in UAV flight trajectory tracking control, and the prediction error of flight trajectory converges to zero,improving the stability and anti disturbance of flight.
出处
《计算机技术与发展》
2018年第1期182-187,共6页
Computer Technology and Development
基金
2015年广东省教育重点平台及科研项目青年创新人才类项目(自然科学类)(2015KQNCX218)
2015年广东省教育重点平台及科研项目特色创新类项目(教育科研类)(2015GXJK185)
2012广东省质量工程项目(粤教高函[2012]204号)
关键词
不确定扰动
气流
无人机
飞行轨迹
跟踪
控制
uncertain disturbance
air flow
UAV
flight trajectory
tracking
control
