A Weak Model Dependent Adaptive Control Method for Flight Vehicle
To mitigate the reliance of flight control design on model accuracy,enhance the robustness of flight control systems under complex environmental disturbances,and broaden the stability design boundaries,an adaptive control method with weak model dependence for aircraft is investigated.This approach is grounded in classical dynamic inverse control principles.In the offline phase,a mapping relationship between model uncertainty and dynamic inverse optimal control gain is established through neural network training.During the online phase,a nonlinear orthogonal recursive least squares method is employed to identify model uncertainty parameters in real time,allowing for online adjustments of the optimal control gain based on neural network outputs.Additionally,a state observer captures identification errors and compensates for their impact on control performance,thereby facilitating adaptive optimization of dynamic inverse control.The robustness and engineering applicability of this weak model adaptive control method are validated through mathematical simulations and flight tests;furthermore,its advantages are demonstrated by comparisons with traditional engineering control methods.
Control of flight vehicleWeak model dependency controlDynamic online identificationOffline neural network
NETL
NSTL
万方数据
