面向失控改出训练的模型预测洗出算法设计

近年来飞机失控已成为飞行事故主要原因，飞行模拟器需要具备失控预防和改出训练功能，因此对洗出算法提出了更高的要求。利用模型预测控制理论对约束处理的优势，结合人体前庭感知模型，建立了基于模型预测控制的洗出算法，采用失速改出这一典型飞机失控情况进行仿真实验，并与经典洗出算法进行了比较，结果表明，模型预测控制洗出算法在约束范围内，提高了仿真逼真度，并具有较好的调整灵活性，对飞行模拟器失控改出训练具有一定应用价值。

Design of Model Predictive Washout Algorithm Towards Upset Recovery Training

In recent years,aircraft out-of-control has become the main cause of flight accidents.Flight simulators need to have upset prevention and recovery training functions,so higher requirements are placed on washout algorithm.Using the advantages of model predictive control theory in dealing with constraints and combining with the hnman vestibular preception model,a washout algorithm based on model predictive control was established.Airplane stall recovery was used as a typical aircraft out-of-control situation to carry out simulation experiment,and compared with the classic washout algorithm.The results show that the model predictive control washout algorithm improves the fidelity of simulation within the scope of the constraints,and has good adjustment flexibility.It has certain application value for upset prevention and recovery training of flight simulators.