Longhao Qian – FLIGHT SYSTEMS and CONTROL

Dr. Longhao Qian is a postdoctoral fellow with the FSC lab. He completed his Ph.D. lab in 2021, and has returned to complete his post-doctoral fellowship in the area of drone trajectory planning, control, and machine learning.

Research Interests

Multi-body Dynamics, Nonlinear Control, Flight Mechanics and Simulation

Currently participating in: Cooperative Payload Delivery with Multiple Quadrotor UAVs

Education

Ph.D. – University of Toronto – Aerospace Science and Engineering (2016 – 2021)
M.A.S. – Shanghai Jiao Tong University – Aeronautics and Astronautics (2013 – 2016)
M.Eng. – University of Toronto – Aerospace Science and Engineering (2014)
B.A.S. – Shanghai Jiao Tong University – Aeronautics and Astronautics (2009 – 2013)

Publications

2023

Qian*, Longhao

Trajectory Control of A Fixed-wing Crop Monitoring Drone: A Case Study Conference

Canadian Aeronautics and Space Institute AERO 23, 2023.

Links | BibTeX

Qian*, Longhao

Robust Stabilization Of A Quadrotor as A 5G Access Point Using Acceleration Feedback Conference

2023 IEEE 9th World Forum on Internet of Things, 2023.

Links | BibTeX

Go*, Shing Hei Helson; Qian*, Longhao; Liu, Hugh HT

Data-Driven and Robust Path-following Control of a Quadrotor Slung Load Transport System Inproceedings

In: AIAA SciTech, 2023.

Abstract | Links | BibTeX

@inproceedings{SciTech2023HG,

title = {Data-Driven and Robust Path-following Control of a Quadrotor Slung Load Transport System},

author = {Shing Hei Helson Go* and Longhao Qian* and Hugh HT Liu},

url = {https://www.flight.utias.utoronto.ca/fsc/wp-content/uploads/2023/05/scitech2023hg_final.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {AIAA SciTech},

journal = {AIAA SciTech},

abstract = {In this paper, a robust path following control law for a quadrotor Slung Load Transport System is developed. A Gaussian Process-augmented Extended Kalman Filter is proposed to estimate payload states. In this approach, Gaussian Processes are used to compensate for unmodelled dynamics in the process model, and they are trained on previously collected data of a Slung Load Transport System in flight. Both simulations and experiments verify the estimation and control system framework and demonstrate successful stabilization and trajectory tracking of the Slung Load Transport System, overcoming model inaccuracy and disturbances. I. Nomenclature F − → = NED inertial frame F − → = Quadrotor body-fixed frame, fixed at the mass center of the quadrotor vehicle = Quadrotor mass J = Quadrotor moment of inertia = Payload mass = Cable length g = Gravity vector in the world frame ∈ R 3 = Vector spanning the length of the cable x ∈ R 3 = Absolute position of the payload mass center v ∈ R 3 = Absolute velocity of the payload mass center, expressed in F − → r ∈ R 2 = Relative position of the quadrotor mass center w.r.t. the payload, projected onto the XY plane of F − → v ∈ R 2 = Relative velocity of the quadrotor mass center w.r.t. the payload, projected onto the XY plane of F − → ˜ v ∈ R 2 = Difference between v predicted by some dynamics model and the true value B ∈ R 3×2 = Projection from v to the time derivative of R ∈ SO(3) = Rotation of F − → relative to F − → ∈ R 3 = Angular velocity of the F − → relative to F − → , expressed in F − → f ∈ R 3 = Total thrust delivered by the actuators ∈ R 3 = Total torque delivered by the actuators d ∈ R 3 = Disturbance acting on the payload d ∈ R 3 = Disturbance acting on the quadrotor body d ∈ R 3 = Sum of disturbances acting on the SLTS e ∈ R 3 = Positional (radial) error of the payload from some given path e ∈ R 3 = Velocity (tangential) error of the payload from some given path × : R 3 → R 3×3 = Maps a 3-vector to a 3-by-3 skew-symmetric (cross product) matrix ∨ : R 3×3 → R 3 = The inverse operation of the × mapping * Ph.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

In this paper, a robust path following control law for a quadrotor Slung Load Transport System is developed. A Gaussian Process-augmented Extended Kalman Filter is proposed to estimate payload states. In this approach, Gaussian Processes are used to compensate for unmodelled dynamics in the process model, and they are trained on previously collected data of a Slung Load Transport System in flight. Both simulations and experiments verify the estimation and control system framework and demonstrate successful stabilization and trajectory tracking of the Slung Load Transport System, overcoming model inaccuracy and disturbances. I. Nomenclature F − → = NED inertial frame F − → = Quadrotor body-fixed frame, fixed at the mass center of the quadrotor vehicle = Quadrotor mass J = Quadrotor moment of inertia = Payload mass = Cable length g = Gravity vector in the world frame ∈ R 3 = Vector spanning the length of the cable x ∈ R 3 = Absolute position of the payload mass center v ∈ R 3 = Absolute velocity of the payload mass center, expressed in F − → r ∈ R 2 = Relative position of the quadrotor mass center w.r.t. the payload, projected onto the XY plane of F − → v ∈ R 2 = Relative velocity of the quadrotor mass center w.r.t. the payload, projected onto the XY plane of F − → ˜ v ∈ R 2 = Difference between v predicted by some dynamics model and the true value B ∈ R 3×2 = Projection from v to the time derivative of R ∈ SO(3) = Rotation of F − → relative to F − → ∈ R 3 = Angular velocity of the F − → relative to F − → , expressed in F − → f ∈ R 3 = Total thrust delivered by the actuators ∈ R 3 = Total torque delivered by the actuators d ∈ R 3 = Disturbance acting on the payload d ∈ R 3 = Disturbance acting on the quadrotor body d ∈ R 3 = Sum of disturbances acting on the SLTS e ∈ R 3 = Positional (radial) error of the payload from some given path e ∈ R 3 = Velocity (tangential) error of the payload from some given path × : R 3 → R 3×3 = Maps a 3-vector to a 3-by-3 skew-symmetric (cross product) matrix ∨ : R 3×3 → R 3 = The inverse operation of the × mapping * Ph.