论文标题
基于多项式混乱的飞行控制优化，并保证了概率性能
Polynomial Chaos-Based Flight Control Optimization with Guaranteed Probabilistic Performance
论文作者
论文摘要
为飞行动态系统提出了基于多项式混乱和优化的概率性绩效控制器设计方法。与保守处理不确定性的强大控制技术不同，该方法旨在有效地传播不确定性并优化控制参数以直接满足概率要求。为了实现这一目标，通过扩展系数和可靠性分析的第四刻方法来评估违规概率的敏感性，此后进行了优化，从而在发生机会限制下进行了最小化故障概率。之后，进行时间依赖性的多项式混沌扩展以验证结果。通过这种方法，在保证闭环性能的同时，降低了故障概率，从而增加了安全边缘。对纵向模型进行模拟，以不确定参数，以证明该方法的有效性。
A probabilistic performance-oriented controller design approach based on polynomial chaos expansion and optimization is proposed for flight dynamic systems. Unlike robust control techniques where uncertainties are conservatively handled, the proposed method aims at propagating uncertainties effectively and optimizing control parameters to satisfy the probabilistic requirements directly. To achieve this, the sensitivities of violation probabilities are evaluated by the expansion coefficients and the fourth moment method for reliability analysis, after which an optimization that minimizes failure probability under chance constraints is conducted. Afterward, a time-dependent polynomial chaos expansion is performed to validate the results. With this approach, the failure probability is reduced while guaranteeing the closed-loop performance, thus increasing the safety margin. Simulations are carried out on a longitudinal model subject to uncertain parameters to demonstrate the effectiveness of this approach.
