Adaptive control method for unmanned vehicle with slung load

摘要

The adaptive control method for an unmanned vehicle with a slung load utilizes a feedback linearization controller (FLC) to perform vertical take off, hovering and landing of an unmanned aerial vehicle with a slung load, such as a quadrotor drone or the like. The controller includes a double loop architecture, where the overall controller includes an inner loop having an inner controller which is responsible for controlling the attitude angles and the altitude, and an outer loop having an outer controller responsible for providing the inner loop inner controller with the desired angle values. States, such as including roll, pitch, yaw and/or altitude, are selected as outputs and the feedback linearization technique is used.

主权项

1. An adaptive control method for an unmanned vehicle with a slung load, comprising the steps of:
establishing a feedback linear controller, such that f(x)=Mb({umlaut over (x)}ad+Λėxa)+Cb({dot over (x)}ad+Λexa)+Db{dot over (x)}a+Gb, wherein f(x) is a control dynamics input for an unmanned aerial vehicle, xad is a vector representing roll, pitch, yaw and altitude for the unmanned aerial vehicle based on a corresponding trajectory, Mb is an inertia matrix associated with the unmanned aerial vehicle, Cb is a centrifugal force and coriolis force matrix associated with the unmanned aerial vehicle, Db is a drag force matrix associated with the unmanned aerial vehicle, and Gb is a gravitational vector, {dot over (x)}a being a velocity vector such thatx.a=(φ.θ.ψ.z.),where φ represents a roll angle of the unmanned aerial vehicle, θ represents a pitch angle of the unmanned aerial vehicle, ψ represents a yaw angle of the unmanned aerial vehicle, and z represents an altitude of the unmanned aerial vehicle, exa representing control error, and Λ being a positive definite constant matrix such that y=ėxa+Λexa, where y is a filtered tracking error, and x is a vector defined asx=[exaT⁢e.xaT⁢xadT⁢x.adT⁢x¨adT]T,where T is a period of a periodic orbit of the slung load;
establishing a two level neural network such that f(x)=WTσ(VTx)+ε, where W and V are neural network weights, σ represents the sigmoid function, and ε being a known bound; calculating a neural network estimate of f(x), {circumflex over (f)}(x), as {circumflex over (f)}(x)=ŴTσ({circumflex over (V)}Tx), where Ŵ and {circumflex over (V)} are actual values of the neural network weights W and V, respectively, given by a tuning algorithm; transmitting a control input τ for the unmanned aerial vehicle as τ=ŴTσ({circumflex over (V)}Tx)+Kvy, where Kv is a feedforward gain, for controlling flight of the unmanned aerial vehicle; and transmitting additional anti-swing control input to the unmanned aerial vehicle to correct for a slung load as xcor=KxcLφL(t−τxc) and ycor=KycLφL(t−τyc), wherein xcor and ycor are additional longitudinal and lateral displacements, respectively, Kxc and Kyc are, respectively, longitudinal and lateral feedback gains, L is a load cable length, φL is a load angle in an x-z plane, t is time, and τxc and τyc are, respectively, longitudinal and lateral time delays introduced in the feedback of the load angle.