Transition Analysis and Practical Flight Control for Ducted Fan Fixed-Wing Aerial Robot: Level Path Flight Mode Transition

Cheng, Zihuan; Pei, Hailong

doi:10.1109/lra.2022.3145087

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…The cascaded control architecture could be similar to that of a multicopter: an outer-loop position controller first computes the desired acceleration, then our differential flatness function maps the desired acceleration to the desired attitude and thrust, and finally the attitude is tracked by an inner-loop attitude controller. Such a cascaded control structure is also used in existing works (Ritz and D’Andrea, 2017; Cheng and Pei, 2022) but based on an over-simplified aerodynamic model.…”

Section: Discussionmentioning

confidence: 99%

“…These approaches are not ideal to agile flights requiring more aggressive control inputs. Consequently, the control performance degrades (i.e., altitude error exceeds 1 m during transition) during highly agile maneuvers as demonstrated in Barth et al (2020) and Cheng and Pei (2022).…”

Section: Tail-sitter Controlmentioning

confidence: 99%

“…The vehicle system is decoupled, approximated, and estimated locally as a group of second-order piece-wise linear systems, and thus the reference thrust and attitude can be solved from the desired body velocity. Similarly, Cheng and Pei (2022) employed an adaptive control law with an IMU-based thrust-attitude decoupling method, assuming zero gradient for the aerodynamic forces. Although model-free methods can estimate and compensate the unmodeled aerodynamics, they apply small control input at each step to maintain the effective region of the state–input linearization.…”

Section: Related Workmentioning

confidence: 99%

“…Our method aims to fully exploit the UAV’s actual aerodynamics to achieve high-accuracy and real-time control performance. Compared to the existing works, our proposed control scheme has the following advantages: (1) Existing works either give up the vehicle aerodynamic model (e.g., model-free methods [Barth et al, 2020; Cheng and Pei, 2022]) or compromise to simplified models (e.g., simplified classic model [Ritz and D’Andrea, 2017], spherical equivalence model [Pucci et al, 2013], steady-level-flight model [Smeur et al, 2020], and ϕ -theory model [Lustosa, 2017; Tal and Karaman, 2022]), while our proposed controller leverages classic aerodynamic models without any simplification on its aerodynamic coefficients. The use of high-fidelity aerodynamic model is crucial to achieve higher control accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Trajectory generation and tracking control for aggressive tail-sitter flights

Lu,

Cai,

Chen

et al. 2023

The International Journal of Robotics Research

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We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate (not simplified) aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints, and actuator saturation. The planned trajectory of flat output is transformed into state trajectory in real time with optional consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The proposed algorithms are implemented on our quadrotor tail-sitter prototype, “Hong Hu,” and their effectiveness are demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10 m/s, typical tail-sitter maneuvers (transition, level flight, and loiter) with speed up to 20 m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight, and Cuban Eight) with acceleration up to 2.5 g. The video demonstration is available at https://youtu.be/2x_bLbVuyrk .

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Section: Discussionmentioning

confidence: 99%

Section: Tail-sitter Controlmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Trajectory generation and tracking control for aggressive tail-sitter flights

Lu,

Cai,

Chen

et al. 2023

The International Journal of Robotics Research

View full text Add to dashboard Cite

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“…Flores provided a desired velocity trajectory and implemented a recurrent neural network-based controller for feedback linearization [12]. Cheng and Pei established a transition corridor based on the constraint of maintaining a fixed altitude [13]. They planned the desired velocity in the corridor and utilized an adaptive controller [14].…”

Section: Introductionmentioning

confidence: 99%

Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs

Zhao

Liu

2023

Drones

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Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications. This study focuses on developing a safe reinforcement learning method for back-transition control between level flight mode and hover mode for ducted-fan tail-sitter UAVs. Our method enables transition control with a minimal altitude change and transition time while adhering to the velocity constraint. We employ the Trust Region Policy Optimization, Proximal Policy Optimization with Lagrangian, and Constrained Policy Optimization (CPO) algorithms for controller training, showcasing the superiority of the CPO algorithm and the necessity of the velocity constraint. The transition trajectory achieved using the CPO algorithm closely resembles the optimal trajectory obtained via the well-known GPOPS-II software with the SNOPT solver. Meanwhile, the CPO algorithm also exhibits strong robustness under unknown perturbations of UAV model parameters and wind disturbance.

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