A unified trajectory optimization framework for lunar ascent. (April 2016)

• Record Type: Journal Article
• Authors: Ma, Lin; Chen, Weifeng; Song, Zhengyu; Shao, Zhijiang
• Is Part Of: Advances in engineering software. Volume 94(2016)
• Page Start: 32
• Page End: 45
• Publication Date: 2016-04
• Subjects: Lunar ascent -- Trajectory optimization -- Fuel-optimal control -- Pseudospectral method -- Homotopy-based backtracking strategy -- Computational guidance and control; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2016.01.002
• Languages: English
• ISSNs: 0965-9978

Abstract

Abstract: This paper presents a unified trajectory optimization framework for lunar ascent. Compared with prevailing and emerging studies on lunar ascent trajectory optimization with simplified lunar ascent process and simple constraints, our method formulates in detail the lunar ascent process with complex constraints in a unified manner. The kinematics and dynamics model of lunar ascent with mission-specific constraints explicitly expressed through equalities or inequalities form the fuel-optimal lunar ascent trajectory optimization problem. A proper direct trajectory optimization method is chosen to transcribe the original trajectory optimization problem into a nonlinear programming (NLP) problem solved by a highly efficient NLP solver. The homotopy-based backtracking initial value strategy is designed to enhance convergence of the solving process. First, a two-phase trajectory optimization problem including vertical-rise phase and orbit-insertion phase is solved in the proposed unified framework. Subsequently, to obtain terrain clearance, we directly incorporate terrain description into the lunar ascent problem to obtain the optimal lunar ascent trajectory. Simulation results indicate that the proposed unified trajectory optimization framework has enough adaptability to efficiently handle complex lunar ascent scenarios. The proposed framework may benefit future autonomous lunar ascent missions.