基于PSO-ELM和改进BACTM的飞机动力学边界分析OA

Aircraft stall,characterized by abrupt lift reduction due to severe aerodynamic flow separation on the wing surface,represents a safety critical phenomenon that can trigger problems such as stall roll and stall spin,posing sig-nificant threats to flight safety.Establishing high-precision dynamic models and conducting stall characteristic analysis are pivotal technical measures for preventing stall risks and enabling effective stall recovery.To address the trade-off between model complexity and accuracy in unsteady aerodynamic force modeling,a hybrid modeling approach inte-grating Particle Swarm Optimization and Extreme Learning Machine(PSO-ELM)was developed.By fusing multi-source wind tunnel test data,this method constructs an unsteady coupled aerodynamic force model with high predic-tive accuracy,low computational complexity,and robust adaptability to diverse operating conditions,thereby enhanc-ing the reliability of stall characteristic analysis and boundary computation.To overcome the limitations of conventional bifurcation analysis algorithms,including initial-value dependency and fixed-step rigidity that hinder thorough explora-tion of solution spaces,a modified bifurcation analysis algorithm incorporating"random state-point generation"and"adaptive step-size adjustment"was proposed.Combined with saddle-node manifold theory,this approach enables global analysis of longitudinal nonlinear dynamic characteristics in aircraft.Using a statically unstable aircraft's longitu-dinal model as a case study,simulations of both open-loop and backstepping control-integrated closed-loop systems were conducted.The focus was on analyzing global longitudinal dynamic behavior and solving stall boundaries,pro-viding dynamic support for mitigating unsteady hysteresis effects,expanding stable flight envelopes,and suppressing unintended stalls.