System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight

Zhou, Weifeng; Chen, Shengyang; Chang, Ching Wei; Wen, Chih-Yung; Chen, Chih-Keng; Li, Boyang

doi:10.1109/access.2020.3042316

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…The signal is sent to the aircraft actuator through an RC transmitter. Doublet inputs are used by researchers and manufacturers to identify aircraft [66][67][68][69][70][71][72][73][74][75][76]. Fig.…”

Section: Flight Data Acquisitionmentioning

confidence: 99%

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Priyambodo

Majid²,

Shouran³

2023

Journal of Robotics and Control

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Vertical take-off and landing (VTOL) is a type of unmanned aerial vehicle (UAV) that is growing rapidly because its ability to take off and land anywhere in tight spaces. One type of VTOL UAV, the tail-sitter, has the best efficiency. However, besides the efficiency offered, some challenges must still be overcome, including the complexity of combining the ability to hover like a helicopter and fly horizontally like a fixed-wing aircraft. This research has two contributions: in the form of how the analytical model is generated and the tools used (specifically for the small VTOL quad tail-sitter UAV) and how to utilize off-the-shelf components for UAV empirical modeling. This research focuses on increasing the speed and accuracy of the UAV VTOL control design in fixed-wing mode. The first step is to carry out analysis and simulation. The model is analytically obtained using OpenVSP in longitudinal and lateral modes. The next step is to realize this analytical model for both the aircraft and the controls. The third step is to measure the flight characteristics of the aircraft. Based on the data recorded during flights, an empirical model is made using system identification technique. The final step is to vali-date the analytical model with the empirical model. The results show that the characteristics of the analytical mode fulfill the specified requirements and are close to the empirical model. Thus, it can be concluded that the analytical model can be implemented directly, and consequently, the VTOL UAV design and development process has been shortened.

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Section: Flight Data Acquisitionmentioning

confidence: 99%

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Priyambodo

Majid²,

Shouran³

2023

Journal of Robotics and Control

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“…After that, Barth et al [8] laid out a control strategy that uses a model-independent technique to stabilize the position of tail-sitter mini-aircraft in the presence of wind perturbations throughout four flight treatments: upward launch, forward motion, hovering, and straight drop. Besides, Zhou et al [9] suggested system identification and model predictive control for a tail-sitter UAV while on a cruise flight. The model was created using least-squares analysis and trust-region techniques, confirmed using outside flying testing, and then utilized for flight simulations [9].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, Zhou et al [9] suggested system identification and model predictive control for a tail-sitter UAV while on a cruise flight. The model was created using least-squares analysis and trust-region techniques, confirmed using outside flying testing, and then utilized for flight simulations [9]. Then, Al-Qassar et al [2] developed optimal sliding mode control (SMC) and super-twisting sliding mode control (STSMC) for the roll behavior of VTOL UAVs in hovering flight with nonparametric uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimized Robust Deadbeat Controller for Roll Motion in Tail-Sitter VTOL UAVs

Mhmood

2024

JISC

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Unmanned Aerial Vehicles (UAVs), have recently sparked attention due to its versatility in a wide range of real-life uses. They require to be controlled so as to conduct different operations and widen their typical roles. This study proposes an optimal robust deadbeat controller for the roll angle motion of tail-sitter vertically take-off and land vehicles, taking into consideration the systems' intrinsic sensitivity to outside influences and fluctuation of their dynamics. Primarily, several assumptions are used to develop an appropriate transfer function that reflects the system physical attributes. The suggested controller is then formed in two sections: the first section addresses the nominal system's unstable dynamics, and the second element imposes the desired deadbeat performance and robustness. The control system variables are optimized using the creative and efficient Incomprehensible but Time-Intelligible Logics optimization technique, ensuring that the specified robustness demand is satisfied correctly. Finally, simulation is used to evaluate the developed controller effectiveness, revealing beneficial stability and performance indicators for both nominal and uncertain regulated system featuring uniform, bounded, and feasible closed-loop outputs. The control unit performs well, with a rising time of 0.0965 seconds, a settling time of 0.1134 seconds, and an overshoot of 0.167%.

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“…In this condition, system transfer functions and statespace models can be identified using input and output data. This process, known as system identification, is widely used for UAVs because of its simplicity and effectiveness [8,12,13]. The limitation of this approach is that the identified result is confined by the state-space model and transfer function, which make it hard to model the complicated nonlinear aerodynamic effects.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Model Predictive Control for a Quadrotor UAV

Jiang

Zhou

et al. 2022

Aerospace

Self Cite

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A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.

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System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight

Cited by 9 publications

References 31 publications

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Design of an Optimized Robust Deadbeat Controller for Roll Motion in Tail-Sitter VTOL UAVs

Neural Network Based Model Predictive Control for a Quadrotor UAV

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