再入飞行器滚动时域轨迹混沌优化设计

Chaotic Optimization Based Receding Horizon Trajectory Design for Reentry Vehicle

针对传统轨迹优化方法计算量比较大需要离线实现,以及对于初值选取比较敏感等问题,提出了一种基于预测控制的临近空间再入飞行器滑翔轨迹设计的实时优化策略。该算法通过将全局非线性优化问题转换成有限时域内的一维变量滚动时域次优化问题,利用均匀采样结合混沌搜索策略,给出了过载、动压和热流等多种过程约束下的次优攻角计算策略。通过合理调整目标函数中不同物理量权重的比例关系,分别得到弹跳形式和平稳滑翔形式的轨迹特征。该算法可以在不提供初始猜测值的情况下在线实现,为几种不同的轨迹形式提供了统一的生成框架;得到的可行次优轨迹曲线还可作为非线性规划方法的初值进一步优化。数学仿真结果说明了该算法的有效性。

Traditional trajectory optimization methods have several drawbacks, such as the time-consuming calculational effort which can only be implemented offline and their sensitivities to the initial guesses of the optimal solutions. A real-time trajectory optimization method is presented for a type of re-entry vehicle in near space based on the model predictive control strategy. The global nonlinear optimization problem can be approximately transformed into an one dimensional variable receding horizon sub-optimal problem in a finite time domain. By using uniform sampling and chaotic search approaches, the sub-optimal command for the angle of attack subject to the multiple constraints such as load factor, dynamic pressure and heat flux is obtained online. By regulating the weighting factors for the different physical variables in the cost function, the trajectory features of skip and glide can be established respectively. This algorithm provides a universal framework for generating distinct trajectory shapes onbeard without initial guesses. The resultant curve may also be a feasible initial guess for other nonlinear programming methods. The simulation results are demonstrated to be effective.