论文标题
使用基于替代物的子集模拟的罕见发生的偶然性约束飞行控制优化
Rare-Event Chance-Constrained Flight Control Optimization Using Surrogate-Based Subset Simulation
论文作者
论文摘要
为飞行系统介绍了一种面向概率性能的控制设计优化方法。为了准确有效地估算稀有事实概率，子集模拟与替代建模技术相结合以提高效率。在子集模拟的每个级别上，都使用接近故障域的样品来构建替代模型。然后逐渐完善现有的代理。作为回报，种子和样本候选者由更新的替代物筛选，从而将大量调用保存到真实的模型并减少计算费用。之后，在罕见的事实限制下优化控制参数，以直接保证系统性能。模拟是在具有参数不确定性的飞机纵向模型上进行的，以证明该方法的效率和准确性。
A probabilistic performance-oriented control design optimization approach is introduced for flight systems. Aiming at estimating rare-event probabilities accurately and efficiently, subset simulation is combined with surrogate modeling techniques to improve efficiency. At each level of subset simulation, the samples that are close to the failure domain are employed to construct a surrogate model. The existing surrogate is then refined progressively. In return, seed and sample candidates are screened by the updated surrogate, thus saving a large number of calls to the true model and reducing the computational expense. Afterwards, control parameters are optimized under rare-event chance constraints to directly guarantee system performance. Simulations are conducted on an aircraft longitudinal model subject to parametric uncertainties to demonstrate the efficiency and accuracy of this method.
