Efficient nonlinear algorithm for drag tracking in entry guidance
In drag acceleration guidance for an entry vehicle,a drag-vs-velocity or drag-vs-energy profile needs to be tracked.If there are modeling errors,tracking laws derived from feedback linearization(FBL)may have poor performances.Tracking laws using nonlinear model predictive control(NMPC)have satisfactory tracking accuracy,but they are computational intensive.A new tracking law concerning both tracking accuracy and computational efficiency is proposed based on NMPC.First,receding-horizon optimization in NMPC is replaced by random searching.Second,at each guidance cycle,corresponding trajectories in the predictive horizon are obtained using random generated constant bank angles,and the commanded bank angle is the value leading to the minimum relative drag error.Third,bank rate constraint is used to shrink the searching space before the startup of random searching.Fourth,a second-order filter is used to estimate the true drag accelerations,and an additional logic modifying the tracking law is adopted to take into account the downrange error.Finally,the new tracking law is tested by the simulation of 500 entry cases with modeling errors.Simulation results indicate that the new tracking law has both high tracking accuracy and high computational efficiency and therefore is more suitable for online missions than previous tracking laws.
Entry guidance Drag-vs-energy profile Nonlinear model predictive control Random searching
Du Xin Li Haiyang Huang Yuechen
National University of Defense Technology,410073 Changsha,Peoples Republic of China
国际会议
2014 Asia-Pacific International Symposium on Aerospace Technology(2014亚太航空航天技术学术会议)
上海
英文
1-12
2014-09-24（万方平台首次上网日期，不代表论文的发表时间）