MPL-CS Based Adaptive Backstepping Method for Post Stall Maneuver Control
Super maneuverability remains an important performance indicator for future aircraft,and the effectiveness of post stall maneuver control will become the key to determining the outcome of close range air combat.In this paper,an adaptive backstepping control method based on minimum parameter learning and cuckoo bird search(MPL-CS)is proposed to solve the problem of poor robustness and low control accuracy caused by serious nonlinearity,coupling and hysteresis in post stall maneuver of advanced layout aircraft.Firstly,based on a complete set of large-scale oscillation wind tunnel test data of an advanced layout aircraft scaled model,an accurate unsteady aerodynamic model of the advanced layout aircraft at high angles of attack was established by improving the Extreme Learning Machine(ELM)method with given modeling accuracy goals.Secondly,an adaptive backstepping method based on MPL was designed to reduce the number of parameters that need to be optimized.Under the influence of uncertainty and model disturbance,the allocation design was completed by combining the daisy-chain allocation method.The key parameters of the adaptive backstepping control law under MPL were optimized based on the CS method.Finally,the classic Cobra maneuver simulation results show that the control accuracy of this method is higher than that of traditional adaptive backstepping methods based on MPL,and it fully considers the actual needs of engineering,with high control accuracy and strong robustness.The method proposed in this paper provides theoretical support and technical path for the post stall maneuver control of future advanced layout aircraft.
post stall maneuverunsteady aerodynamicCuckoo searchminimum parameter learningadaptive backstepping control
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