Tailless aircraft control law design using dynamic inversion & μ-synthesis

Abstract Backstepping represents a promising control law for fixed-wing Unmanned Aerial Vehicles (UAVs). Its non-linearity and its adaptation capabilities guarantee adequate control performance over the whole flight envelope, even when the aircraft model is affected by parametric uncertainties. In the literature, several works apply backstepping controllers to various aspects of fixed-wing UAV flight. Unfortunately, many of them have not been implemented in a real-time controller, and only few attempt simultaneous longitudinal and lateral–directional aircraft control. In this paper, an existing backstepping approach able to control longitudinal and lateral–directional motions is adapted for the definition of a control strategy suitable for small UAV autopilots. Rapidly changing inner-loop variables are controlled with non-adaptive backstepping, while slower outer loop navigation variables are Proportional–Integral–Derivative (PID) controlled. The controller is evaluated through numerical simulations for two very diverse fixed-wing aircraft performing complex manoeuvres. The controller behaviour with model parametric uncertainties or in presence of noise is also tested. The performance results of a real-time implementation on a microcontroller are evaluated through hardware-in-the-loop simulation.

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Robust control law design for lateral-directional modes of an F-16/MATV using μ-synthesis and dynamic inversion

International Journal of Robust and Nonlinear Control ◽

10.1002/(sici)1099-1239(199708)7:8<777::aid-rnc239>3.0.co;2-d ◽

1997 ◽

Vol 7 (8) ◽

pp. 777-795 ◽

Cited By ~ 14

Author(s):

William C. Reigelsperger ◽

Kelly D. Hammett ◽

Siva S. Banda

Keyword(s):

Robust Control ◽

Dynamic Inversion ◽

Control Law

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Application of evolutionary computation to aircraft control law design

Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546) ◽

10.1109/cec.2001.934306 ◽

2002 ◽

Cited By ~ 1

Author(s):

F.O. Heimes ◽

W.S. Ford ◽

W. Land

Keyword(s):

Evolutionary Computation ◽

Control Law ◽

Aircraft Control

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Design of Automatic Landing Systems Using the H-inf Control and the Dynamic Inversion

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4032028 ◽

2015 ◽

Vol 138 (2) ◽

Cited By ~ 12

Author(s):

Romulus Lungu ◽

Mihai Lungu

Keyword(s):

Federal Aviation Administration ◽

Dynamic Inversion ◽

Control Law ◽

Reference Models ◽

Low Intensity ◽

Automatic Landing ◽

Sensor Measurement ◽

Wind Shears ◽

Sensor Errors ◽

Theoretical Results

This paper focuses on the automatic control of aircraft in the longitudinal plane, during landing, by using the linearized dynamics of aircraft, taking into consideration the wind shears and the errors of the sensors. A new robust automatic landing system (ALS) is obtained by means of the H-inf control, the dynamic inversion, an optimal observer, and two reference models providing the aircraft desired velocity and altitude. The theoretical results are validated by numerical simulations for a Boeing 747 landing; the simulation results are very good (Federal Aviation Administration (FAA) accuracy requirements for Category III are met) and show the robustness of the system even in the presence of wind shears and sensor errors. Moreover, the designed control law has the ability to reject the sensor measurement noises and wind shears with low intensity.

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Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster

Complexity ◽

10.1155/2019/1645042 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 5

Author(s):

Mihai Lungu ◽

Romulus Lungu

Keyword(s):

Neural Network ◽

Attitude Control ◽

Approximation Error ◽

Inversion Method ◽

Dynamic Inversion ◽

Control Law ◽

Feed Forward Neural Network ◽

Small Satellites ◽

Control Moment ◽

Satellite Attitude Control

The paper presents an adaptive system for the control of small satellites’ attitude by using a pyramidal cluster of four variable-speed control moment gyros as actuators. Starting from the dynamic model of the pyramidal cluster, an adaptive control law is designed by means of the dynamic inversion method and a feed-forward neural network-based nonlinear subsystem; the control law has a proportional-integrator component (for the control of the reduced-order linear subsystem) and an adaptive component (for the compensation of the approximation error associated with the function describing the dynamics of the nonlinear system). The software implementation and validation of the new control architecture are achieved by using the Matlab/Simulink environment.

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