Powered Descent Guidance Algorithm Research Articles

Adaptive powered descent guidance based on multi-phase pseudospectral convex optimization

The real-time and fuel-optimal powered descent guidance is one of the critical technologies for planetary landing. This work focuses on the powered descent guidance algorithm based on pseudospectral convex optimization and aims to increase the accuracy and adaptability of the algorithm. The entire powered descent process is divided into phases according to the thrust switching times, and the pseudospectral method is adopted to discretize the optimization problem. Convexification process is performed to transform the optimization problem into a convex programming problem, and a successive solving process algorithm is proposed. For the established multi-phase pseudospectral convex optimization formulation, the indirect method is adopted to derive and determine the optimal phase division from switching function, and the adaptive multi-phase pseudospectral convex optimization method is proposed. Numerical examples demonstrate that the proposed method has excellent accuracy and adaptability.

Multi-point powered descent guidance based on optimal sensitivity

Next-generation powered descent guidance (PDG) for planetary soft landing needs multi-point-guidance capabilities. Multi-point-guidance capabilities involve two parts: landing site selection and trajectory generation. A novel multi-point PDG algorithm is presented in this study. A modified K-means clustering method and optimal sensitivity based on Interior Point OPTimizer (IPOPT) are utilized to select the fuel-optimal landing site from a list of candidate landing sites. The cluster radius is determined by offline calculation. During the selection of the fuel-optimal landing site, estimated trajectories to each candidate landing site are generated by sensitivity analysis. The optimized trajectory to the best landing site can be efficiently obtained based on a good guess of initial value provided by the estimated trajectory. Theoretical error analysis is given for the proposed guidance algorithm. Numerical results indicate that the proposed guidance algorithm can effectively and efficiently address the multi-point powered descent problem.

Optimal nonlinear feedback guidance algorithm for Mars powered descent

In this paper, an optimal nonlinear feedback guidance algorithm with complex state and control constraints is developed for Mars powered decent. The analysis of the optimal control problem for Mars powered descent is undertaken firstly. Then based on the real-time sampling optimal feedback control theory, the Mars powered descent guidance (PDG) algorithm is designed and analysed. A practical method is also proposed to solve the problem of the initialization of the PDG algorithm. Numerical simulations are performed to evaluate the effectiveness of the proposed PDG algorithm. The effects of the sampling period and the prediction errors on landing errors are studied in the numerical simulations. The fuel consumption performances of the proposed PDG algorithm and the Apollo guidance algorithm are also studied and compared. The simulation results show that the less fuel consumption is obtained with the proposed PDG algorithm. Monte Carlo simulation verifies the high landing precision of the proposed PDG algorithm.