Multi-stage trajectory planning of dual-pulse missiles considering range safety based on sequential convex programming and artificial neural network

Abstract

An algorithm based on sequential convex programming and artificial neural networks is proposed to solve the multi-stage trajectory planning problem of dual-pulse missiles considering range safety. Besides nonlinear dynamics and constraints, the dual-pulse missile introduces many discrete optimization variables (such as the ignition time of the second-stage thrust), and the algorithm needs to consider throwing the engine off to a safe location to ensure range safety, which makes trajectory planning for the dual-pulse missile more difficult to solve. In this study, the whole trajectory is first divided into four stages according to the working characteristics of the dual-pulse engine. Second, three new control variables are introduced to realize nonlinear dynamics convexification, and the relaxation technique is used to relax the constraints between the control variables to avoid non-convexity. Then, a range prediction function is designed to predict the landing location of the engine in real time. To improve the real-time prediction speed, an artificial neural network is further used to fit the range prediction function. Finally, an algorithm combining sequential convex optimization and an artificial neural network is proposed to solve the multi-stage trajectory planning problem of dual-pulse missiles accurately and rapidly. By comparing with pseudospectral method, two trajectory planning cases are solved by simulation, and the effectiveness and rapidity of the proposed method are verified.

Robust Trajectory Planning Of Gliding-Guided Projectiles With Weak Maneuverability

“…He employed swarm intelligence algorithms such as the grey wolf optimizer, firefly algorithm, and particle swarm optimization for maximizing the maneuver characteristics of cruise missiles, and compared their respective advantages and disadvantages [8]. Liu introduced a multi-stage rapid trajectory planning algorithm based on sequential convex optimization for the online planning of multi-stage trajectories [9]. Beyond planning for individual projectiles, Liu and Pinon separately researched collaborative multiple-projectile and 'autonomous actors-artillery collaboration' trajectory planning [10,11].…”

Polytopal approximation for thrust magnitude constraints in rocket trajectory optimization

“…References 13–15 developed the lossless convexification theory for specific class of rocket trajectory optimization problems. References 4,16–24 extended the convex programming approach to more general problems with sequential convexification. These works are built upon second-order cone programming (SOCP) solvers that implement the state-of-the-art interior-point methods, which have polynomial-time complexity and high computational efficiency.…”