Parallel optimal guidance for obstacle avoidance and impact angle constraints

For high-value offensive precision-strike missions in complex battlefield environments, a robust parallel optimal guidance method considering obstacle-avoidance, flight-time, and impact-angle constraints is proposed. First, to accurately describe the characteristics of obstacles, a three-dimensional cylinder-hemisphere model is constructed, and the LogSumExp function is employed to obtain a smooth representation of the obstacle avoidance constraint. Furthermore, an optimization problem incorporating trajectory length and smoothness indices is formulated, and a time-parameterized optimal trajectory satisfying the obstacle avoidance constraint and the terminal impact angle constraint is generated based on Bézier curves. On this basis, the optimal trajectory is taken as the reference trajectory, and a robust parallel optimal guidance method with composite learning capability is designed by combining the idea of single-network reinforcement learning; in this method, historical data is used to update the weights of the critic network, thereby improving the optimal control law online, relaxing the persistent excitation condition, and eliminating the dependence on an initial admissible control policy. Through Lyapunov stability theory, the uniform ultimate boundedness of the closed-loop system is analyzed and proved. Simulation results show that the proposed method can obtain a safe, smooth, and feasible trajectory with the desired terminal impact angle in complex obstacle environments, and achieve high-precision tracking of the reference trajectory, thereby verifying its effectiveness in complex constrained scenarios.