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Summary This paper studies the optimal ascent trajectory for a one‐stage‐to‐orbit, air‐breathing vehicle. This kind of vehicle allows endoatmospheric lateral maneuver and thus can extend the launch window. To obtain the optimal orbit‐insertion trajectory in consideration of launch time, a novel optimization model is proposed by designing longitudinal and lateral profiles separately. The longitudinal flight aims to meet the orbital shape that contains the eccentricity and the angular momentum, which are transformed to boundary constrains in altitude, path angle, and velocity. For lateral motion, the orbit inclination and right ascension of ascending node are designed to vary simultaneously as the range. By designing an altitude profile, the terminal altitude and path angle are naturally met and removed from the constraints and accurate orbit insertion is realized by searching the total range and the initial azimuth. Then, the original problem is converted to a parameter optimization problem and solved by a hybrid optimization algorithm, in which the solution is first searched by a particle swarm optimization method and then refined by a modified golden section method. Simulation tests the proposed method with various scenarios. Compared to traditional launch vehicles, a launch window over 1.5 hours is derived without consuming the propellant in the upper stage.

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This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.