Backstepping - Sliding Mode Controllers Applied to a Fixed-Wing UAV

Espinoza, Tadeo; Dzul, A.; Lozano, Rogelio; Parada, P.

doi:10.1007/s10846-013-9955-y

Cited by 54 publications

(31 citation statements)

References 7 publications

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“…Several control techniques based on linearised models obtained under the assumption of low angle-of-attach (AOA) have been proposed, e.g. (Espinoza et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

LPV model reference control for fixed-wing UAVs

et al. 2017

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This paper proposes a linear parameter varying (LPV) model reference-based control for fixed-wing unmanned aerial vehicles (UAVs), which achieves agile and high performance tracking objectives in extended flight envelopes, e.g. when near stall or deep stall flight conditions are considered. Each of the considered control loops (yaw, pitch and airspeed) delivers an error model that can be reshaped into a quasi-LPV form through an appropriate choice of the scheduling variables. The quasi-LPV error models are suitable for designing error feedback controllers using linear matrix inequalities (LMIs), which are derived within the quadratic Lyapunov framework. Simulation results are used to show the effectiveness of the proposed approach.

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“…Several control techniques based on linearised models obtained under the assumption of low angle-of-attach (AOA) have been proposed, e.g. (Espinoza et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

LPV model reference control for fixed-wing UAVs

et al. 2017

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“…Two control techniques, sliding mode control (SMC) [10], [11] and L 1 adaptive control [12], are claimed to offer robust properties against matched uncertainties. The performance of SMC for attitude control of a fixedwing UAV is investigated in [13] where SMC is able to handle partial loss of a control surface. To make the system fault tolerant against the total loss of a control surface, a sliding mode observer is introduced in [14], making it possible to estimate a specific actuator fault.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Controllers with Fault-Dependent Control Allocation for UAVs

Sørensen

Hansen

Breivik

et al. 2017

J Intell Robot Syst

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This paper combines fault-dependent control allocation with three different control schemes to obtain fault tolerance in the longitudinal control of unmanned aerial vehicles. The paper shows that faultdependent control allocation is able to accommodate actuator faults that would otherwise be critical and it makes a performance assessment for the different control algorithms: an L 1 adaptive backstepping controller; a robust sliding mode controller; and a standard PID controller. The actuator faults considered are the partial to total loss of the elevator, which is a critical component for the safe operation of unmanned aerial vehicles. During nominal operation, only the main actuator, namely the elevator, is active for pitch control. In the event of a partial or total loss of the elevator, faultdependent control allocation is used to redistribute control to available healthy actuators. Using simulations of a Cessna 182 aircraft model, controller performance and robustness are evaluated by metrics that assess control accuracy and energy use. System uncertainties are investigated over an envelope of pertinent variation, showing that sliding mode and L 1 adaptive backstepping provide robustness, where PID control falls short. Additionally, a key finding is that the fault-dependent

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“…Successively, command filtered back stepping (CFBS) has been proposed by Farrell [6] [7] that eliminated the requirement of analytical model derivation and simplified its control design. However, introducing the Wulung UAV model to this controller needs a nonlinear model in the form of strict-feedback systems [8] [9] that separates the terms of the fixed-wing UAV control variables as shown in Figure 2 and expressed as the following model:…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Modeling of a Fixed-Wing Unmanned Aerial Vehicle: a Case Study of Wulung

Triputra

Trilaksono

Adiono

et al. 2015

J. Mechatron. Electr. Power Veh. Technol.

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Developing a nonlinear adaptive control system for a fixed-wing unmanned aerial vehicle (UAV) requires a mathematical representation of the system dynamics analytically as a set of differential equations in the form of a strict-feedback systems. This paper presents a method for modeling a nonlinear flight dynamics of the fixed-wing UAV of BPPT Wulung in any conditions of the flight altitude and airspeed for the first step into designing a nonlinear adaptive controller. The model was formed into 10-DOF differential equations in the form of strict-feedback systems which separates the terms of elevator, aileron, rudder, and throttle from the model. The model simulation results show the behavior of the flight dynamics of the Wulung UAV and also prove the compliance with the actual flight test results.

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Backstepping - Sliding Mode Controllers Applied to a Fixed-Wing UAV

Cited by 54 publications

References 7 publications

LPV model reference control for fixed-wing UAVs

LPV model reference control for fixed-wing UAVs

Performance Comparison of Controllers with Fault-Dependent Control Allocation for UAVs

Nonlinear Dynamic Modeling of a Fixed-Wing Unmanned Aerial Vehicle: a Case Study of Wulung

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