Trajectory Optimization of Long-Range Guided Rocket-Propelled Vehicle Based on Pseudo-spectral Sequential Convex Programming

Abstract

Aiming at the trajectory optimization problem of long-range guided rocket-propelled vehicles with multiple constraints, an improved pseudo-spectral sequence convex programming, which takes into account the efficiency and accuracy of optimization, is proposed. First, taking the change rate of the angle of attack and the bank angle as the control variables, the vehicle’s dynamic equation is discretized by using the flipped Radau pseudo-spectral method, and the trajectory optimization problem including start, end, and path constraints is transformed into a convex optimization problem by using the continuous linearization method, and then the solution is obtained by using the original dual interior point method. To make the optimized trajectory near the no-fly zone or the severe weather area more accurate, the concept of the predicted separation point is introduced to subdivide the trajectory. The simulation results show that the improved pseudo-spectral sequence convex programming has high discretization accuracy and fast solution speed, and can be applied to the online use of long-range guided rocket-propelled vehicles.