组合动力运载器上升段轨迹智能优化方法

针对组合动力水平起飞可重复使用运载器，开展了上升段轨迹优化模型设计与轨迹优化方法研究。首先，针对跨大空/速域飞行须采用多种动力形式协调工作这一问题，考虑动力/气动/轨迹/指标间的复杂耦合关系，建立了运载器动力和气动模型。其次，为降低轨迹优化问题的求解难度，设计了一种全新的飞行剖面，实现了关键优化参数的提取和攻角约束的自动满足，减少了优化算法需要处理的约束数量。然后，提出了一种改进的粒子群优化(PSO)算法完成求解；在收敛性分析的基础上，引入强化学习机制对PSO寻优过程进行自主智能控制，从本质上提升了PSO算法的求解效率。最后通过数学仿真验证了方法的正确性和有效性。

Ascent Trajectory Optimization for a Multi Combined Cycle Based Launch Vehicle Using a Hybrid Heuristic Algorithm

This paper researches the ascent trajectory optimization model and the optimization method for a reusable launch vehicle, which takes off horizontally, and climbs and accelerates using a multi-combined-cycle-based engine. Firstly, in consideration of the fact that the synergistic work of multiple types of engines is required to accomplish the ascent flight, and the complicated nonlinear coupling among propulsion, aerodynamics, trajectory, and performance index, the mathematical models of the propulsion and aerodynamic coefficients are proposed. Secondly, in order to reduce the nonlinear coupling, a novel ascent flight profile is proposed; in this way, the key optimization parameters are picked out, the trajectory constraints are easily satisfied, and the number of the constraints tackled by the optimization method is reduced. Thirdly, an improved particle swarm optimization (PSO) method is proposed to optimize the trajectory. Based on the analysis of the convergence of PSO, a reinforcement learning mechanism is introduced into PSO to automatically, intelligently, and adaptively control the searching process of PSO. The numerical simulation indicates the efficiency of the proposed method.