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Neural network and fuzzy logic-based hybrid attitude controller designs of a fixed-wing UAV

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Abstract

In this paper, a mini unmanned aerial vehicle (UAV) is planned to be used in applications such as spraying pesticide and weed control in agricultural areas. According to literature review, proportional + integral + derivative (PID) structure is used to control many of these UAVs. This controller is insufficient against uncertain weather conditions and disturbance effects. In this study, many different control techniques are evaluated to select the controller structure that can respond to these uncertainties. The structure having the best result was chosen as the UAV controller. Ultrastick-25e mini UAV model is used to control the roll and yaw angle lateral dynamics. State-space presentation of the UAV longitudinal and lateral dynamics is explained, and it is just obtained for the lateral dynamics to control the attitude of the UAV under 60 km/h flight velocity condition. According to the aileron and rudder inputs, lateral dynamics simulations have successfully done by using five different controller methods such as classical PID, artificial neuro-fuzzy inference system (ANFIS), fuzzy logic controller, combined ANFIS-PID, and PD-Fuzzy-PI controllers. Moreover, three different input signals are assumed to evaluate the system response. Additionally, transient response and the time performance parameters such as overshoots, peak, rise and settling times, and steady-state error have analyzed for the designed different controllers. The simulated results for the five different controller designs showed that combined PD-fuzzy-PI and ANFIS-PID controllers have more acceptable performance than other controllers at the steady level flight condition. It is aimed that the simulation findings obtained in this study will contribute to experimental studies.

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Abbreviations

AI:

Artificial intelligence []

ANFIS:

Artificial neuro-fuzzy inference system []

AWGN:

Additive Gaussian white noise []

EOM:

Equations of motion []

FIS:

Fuzzy inference system []

FLC:

Fuzzy logic controller []

GA:

Genetic algorithm []

GPS:

Global positioning system []

LQR:

Linear quadratic regulator []

x b, y b, z b :

Aircraft body coordinates []

p, q, r :

Roll, pitch, yaw rates in aircraft coordinates (rad−1)

u, v, w :

Linear velocities in aircraft coordinates (m/s)

ϕ, θ, ψ :

Euler angles of the aircraft body (rad)

X u, X w, Z u, Z w :

Force derivatives for states u, w (s−1)

M u, M w :

Moment derivatives for states u, w (1/m s)

M q :

Moment derivative for state q (1/s)

Xδe, XδT, Zδe, ZδT :

Force derivatives due to elevator deflection and throttle change (1/s2)

Mδe, MδT :

Moment derivatives due to elevator and throttle change (1/s2)

∆δe, ∆δT :

Elevator deflection and change in thrust (rad)

ARw :

Aspect ratio []

b :

Wingspan (m)

\(\bar{c}\) :

Wing mean aerodynamic chord (m)

d :

Maximum depth of the fuselage (mm)

I xx, I yy, I zz :

UAV inertial moments about the x–y–z axes (kgm2)

I xz :

x, z axes product moment of inertia (kgm2)

l t :

Distance from cg to 1/4 chord of tail (m)

S, S r :

Wing area and reference area (m2)

S t, S vt :

Horizontal and vertical tail area (m2)

u 0 :

UAV speed (m/s)

\(\frac{{x_{ac} }}{{\bar{c}}}\) :

Dimensionless interval from wing leading edge to aerodynamic center []

Z T :

Interval parallel to z-axis from thrust centerline to cg of the UAV (mm)

Z vt :

Parallel to z-axis from vertical tail center of pressure to fuselage centerline (mm)

Z w :

Interval parallel to the z-axis from ¼ chord to the fuselage centerline (mm)

LSE:

Least square error []

MF:

Membership function []

NB, NS:

Negative big, negative small []

PB, PS:

Positive big, positive small []

PID:

Proportional, integral, derivative []

SNR:

Signal-to-noise ratio []

RMSE:

Root mean square error []

UAV:

Unmanned aerial vehicle []

ZN:

Ziegler–Nichols []

C D0, C Du :

UAV reference drag factor, drag factor []

C Dα :

UAV drag graph slope (rad−1)

C L0, C Tu :

UAV reference lift factor, thrust stability factor []

C Lα, C Lαw :

UAV lift, wing lift graph slope (rad−1)

C Lαt, C Lαvt :

Horizontal, vertical tail lift graph slope (rad−1)

C mα, \(C_{{m\dot{\alpha }}}\) :

Pitching moment variation of alpha, alpha rate (rad−1)

C yβ, C yp, C yr :

Side force variation due to beta, roll rate, yaw rate []

C nβ, C np, C nr :

Yaw moment due to beta, roll rate, yaw rate []

C lβ, C lp, C lr :

Roll moment due to beta, roll rate, yaw rate []

C Yδa, C Yδr :

Side force due to aileron, rudder deflections []

C nδa, C nδr :

Yaw moment due to aileron, rudder deflections []

C lδa, C lδr :

Roll moment due to aileron, rudder deflections []

d i :

Prop blade diameter (in)

m :

UAV mass (kg)

Q :

Dynamic pressure [60 km/h] (N/m2)

rpm:

Propeller angular speed (1/min)

pitch:

Propeller pitch (in)

V H :

Horizontal tail–volume ratio []

y 1, y 2 :

Aileron start, end points (m)

λ :

Taper ratio wing []

\(\frac{{x_{{\text{cg}}} }}{{\bar{c}}}\) :

Dimensionless interval from wing leading edge to the cg []

η, η vt :

Tail and vertical tail effectiveness []

Λ, Λ c/4w :

Wing sweep, wing sweep at 1/4 chord length (rad)

τ a, τ e, τ r :

Aileron, elevator, rudder effectiveness []

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Acknowledgement

This study is a piece of work of the research project numbered as FBA-2018-8391. The authors would like to show their appreciation for economical support being supplied by the Scientific Research Project Coordination Unit of Erciyes University, in accomplishing this study.

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  1. Mechatronic Engineering Department, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey

    Şaban Ulus & İkbal Eski

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  1. Şaban Ulus
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Ulus, Ş., Eski, İ. Neural network and fuzzy logic-based hybrid attitude controller designs of a fixed-wing UAV. Neural Comput & Applic 33, 8821–8843 (2021). https://doi.org/10.1007/s00521-020-05629-5

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