Near‐Optimal Guidance with Impact Angle and Velocity Constraints Using Sequential Convex Programming

Abstract

This paper proposes a near-optimal air-to-ground missile guidance law with impact angle and impact velocity constraints based on sequential convex programming. A realistic aerodynamic model is introduced into the problem formulation, such that traditional optimization theory cannot obtain an analytical solution to the optimization problem under state constraints. The original problem is considered as an optimization problem, and the angle of attack is replaced with the angle of attack rate as a new control variable to reconstruct the problem and simplify the solving process. Next, the independent variable is changed in the differential equations to linearize and discretize the problem such that the reconstructed problem can be solved using sequential convex programming. The results obtained by numerical simulations confirmed that the proposed algorithm is valid and faster than the general-purpose nonlinear optimal control problem solver. Finally, it was verified that different impact angles and impact velocities were achieved.

An Online Generation Method of Terminal-Area Trajectories for Wave-Rider Using Deep Neural Networks

“…The linearization of the dynamics equations is performed within the neighborhood of the optimized trajectory obtained in the previous optimization iteration. Solving a trajectory optimization problem requires conducting multiple rounds of convex optimization sequentially, which are named sequential convex optimization [25][26][27][28][29][30][31][32][33][34][35][36][37].…”

A New Optimal Guidance Law with Impact Time and Angle Constraints Based on Sequential Convex Programming

“…Besides the previous traditional analytical form of guidance laws, the computational guidance law has been studied in [13][14][15]. In fields of plant entry [16,17], spacecraft descent guidance [18,19], rendezvous and proximity [20],orbit transfer problem [21], powered descent and landing [22], trajectory optimization [23][24][25], etc., convex programming has been verified that it can solve nonlinear optimization problem efficiently and accurately. Liu [26] has used convex programming to derive a time-varying PN guidance law for impact angle constraint.…”