Lyapunov‐based trajectory tracking controller for a fixed‐wing unmanned aerial vehicle in the presence of wind

Brezoescu, A.; Lozano, Rogelio; Castillo, Pedro

doi:10.1002/acs.2480

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…Let the initial time t 0 be satisfied with t 0 = 0 s. Parameters of the desired trajectory (9) and 5 m, respectively. For the ground vehicle (5) and the quadrotor (6), there exists inevitable errors on state variables between target position and actual position at the initial time t 0 .…”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive cooperative control for air‐ground systems with actuator saturation under disturbances

Yang

Wang

et al. 2022

Adaptive Control & Signal

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Summary In this article, an adaptive cooperative control strategy is proposed for an air‐ground system with actuator saturation. The air‐ground system with actuator saturation includes a ground vehicle with road bumps and a quadrotor with gust winds. The adaptive cooperative control strategy is composed of four tracking differentiators, four adaptive extended state observers and two adaptive integral SMC laws. Based on Silverman canonical transformation and pole placement, the adaptive extended state observers are designed to estimate disturbances from the road bumps and gust winds. The adaptive integral SMC laws are proposed to achieve cooperation between the ground vehicle and the quadrotor in the air‐ground system with actuator saturation. Simulation results are provided to show effectiveness of the adaptive cooperative control strategy by a ground vehicle and a quadrotor.

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Section: Simulation Resultsmentioning

confidence: 99%

Adaptive cooperative control for air‐ground systems with actuator saturation under disturbances

Yang

Wang

et al. 2022

Adaptive Control & Signal

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“…According to the analysis of the lateral symmetry plane, the influence of aerodynamics is mainly related to the rudder (𝑈 2 ) , aileron steering gear (𝑈 3 ) , yaw angular velocity(𝑝), roll angular velocity (𝑟) and sideslip angle (𝛽). Their relationship is as follows [31]:…”

Section: Aerodynamic Model and Thrust Model 1) Establishment Of Aerodynamic Modelmentioning

confidence: 99%

Design of a Fixed-Wing UAV Controller Based on Adaptive Backstepping Sliding Mode Control Method

Bao

Guo²,

Luo

et al. 2021

IEEE Access

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“…Adaptive control can be a good alternative to robust control in certain cases [7]. A popular topic for research is the combination of robust and adaptive control methods [3,[7][8][9][10], though the methods proposed are often highly complex and unique to the intended applications. Another area of active research is that of model-free robust control [3,[11][12][13][14][15][16][17][18], since one of the main drawbacks of sliding mode control and other methods is the time-consuming control law derivation process that is required for each implementation [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Model-Free Sliding Mode Control Algorithms including Application to a Real-World Quadrotor

Schulken¹,

Crassidis²

2018

International Conference of Control, Dynamic Systems, and Robotics

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Sliding mode control is a robust nonlinear control algorithm that has been used to implement tracking controllers for unmanned aircraft systems that are robust to modeling uncertainty and exogenous disturbances, thereby providing excellent performance for autonomous operation. A significant advance in the application of sliding mode control for unmanned aircraft systems would be adaptation of a model-free sliding mode control algorithm, since the most complex and time-consuming aspect of implementation of sliding mode control is the derivation of the control law with incorporation of the system model, a process required to be performed for each individual application of sliding mode control. The performance of various model-free sliding mode control algorithms was compared in simulation using a variety of aerial system models and real-world disturbances (e.g. the effects of discretization and state estimation). The two best performing algorithms were shown to exhibit very similar behavior. These two algorithms were implemented on a quadrotor (both in simulation and using real-world hardware) and the performance was compared to a traditional PID based controller using the same state estimation algorithm and control setup. Simulation results show the model-free sliding mode control algorithms exhibit similar performance to PID controllers without the tedious tuning process. Comparison between the two model-free sliding mode control algorithms showed very similar performance as measured by the quadratic means of tracking errors. Flight testing showed that while a model-free sliding mode control algorithm can control real-world hardware, further characterization and significant improvements are required before it is a viable alternative to conventional control algorithms. Large tracking errors were observed for both the model-free sliding mode control and PID based flight controllers and the performance was characterized as unacceptable for most applications. The poor performance of both controllers suggests tracking errors can be attributed to errors in state estimation. Further testing with improved state estimation would allow for more conclusions to be drawn.

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Lyapunov‐based trajectory tracking controller for a fixed‐wing unmanned aerial vehicle in the presence of wind

Abstract: International audienc

Cited by 15 publications

References 20 publications

Adaptive cooperative control for air‐ground systems with actuator saturation under disturbances

Adaptive cooperative control for air‐ground systems with actuator saturation under disturbances

Design of a Fixed-Wing UAV Controller Based on Adaptive Backstepping Sliding Mode Control Method

Model-Free Sliding Mode Control Algorithms including Application to a Real-World Quadrotor

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