Neural network compound learning non-backstepping control method for hypersonic vehicle
A hypersonic, neural network technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as poor practicability
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[0066] refer to figure 1 . The specific steps of the hypersonic vehicle neural network compound learning non-backstepping control method of the present invention are as follows:
[0067] (a) Establish a hypersonic vehicle longitudinal channel dynamics model:
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[0073] The dynamic model is composed of five state variables X=[V, h, α, γ, q] T and two control inputs U=[δ e ,β] T Composition; where, V represents velocity, γ represents track inclination, h represents height, α represents angle of attack, q represents pitch angular velocity, δ e is the rudder deflection angle, β is the throttle valve opening; T, D, L and M yy Represent thrust, drag, lift and pitching torque respectively; m, I yy , μ and r represent the mass, the moment of inertia of the pitch axis, the gravitational coefficient and the distance from the center of the earth;
[0074] The relevant torque and parameters are defined as follows:
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