40 research outputs found
Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness
Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e.,
high-speed and high-acceleration) maneuvers have attracted significant
attention in the past few years. This paper focuses on accurate tracking of
aggressive quadcopter trajectories. We propose a novel control law for tracking
of position and yaw angle and their derivatives of up to fourth order,
specifically, velocity, acceleration, jerk, and snap along with yaw rate and
yaw acceleration. Jerk and snap are tracked using feedforward inputs for
angular rate and angular acceleration based on the differential flatness of the
quadcopter dynamics. Snap tracking requires direct control of body torque,
which we achieve using closed-loop motor speed control based on measurements
from optical encoders attached to the motors. The controller utilizes
incremental nonlinear dynamic inversion (INDI) for robust tracking of linear
and angular accelerations despite external disturbances, such as aerodynamic
drag forces. Hence, prior modeling of aerodynamic effects is not required. We
rigorously analyze the proposed control law through response analysis, and we
demonstrate it in experiments. The controller enables a quadcopter UAV to track
complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to
2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a
flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the
robustness of the controller by attaching a drag plate to the UAV in flight
tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology.
Revision: new set of experiments at increased speed (up to 12.9 m/s), updated
controller design using quaternion representation, new video available at
https://youtu.be/K15lNBAKDC
A flow disturbance estimation and rejection strategy for multirotors with round-trip trajectories
This paper presents a round-trip strategy of multirotors subject to unknown
flow disturbances. During the outbound flight, the vehicle immediately utilizes
the wind disturbance estimations in feedback control, as an attempt to reduce
the tracking error. During this phase, the disturbance estimations with respect
to the position are also recorded for future use. For the return flight, the
disturbances previously collected are then routed through a feedforward
controller. The major assumption here is that the disturbances may vary over
space, but not over time during the same mission. We demonstrate the
effectiveness of this feedforward strategy via experiments with two different
types of wind flows; a simple jet flow and a more complex flow. To use as a
baseline case, a cascaded PD controller with an additional feedback loop for
disturbance estimation was employed for outbound flights. To display our
contributions regarding the additional feedforward approach, an additional
feedforward correction term obtained via prerecorded data was integrated for
the return flight. Compared to the baseline controller, the feedforward
controller was observed to produce 43% less RMSE position error at a vehicle
ground velocity of 1 m/s with 6 m/s of environmental wind velocity. This
feedforward approach also produced 14% less RMSE position error for the complex
flows as well
Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this letter, we propose L1 -NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline
Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure
The expectation of unmanned air vehicles (UAVs) pushes the operation
environment to narrow spaces, where the systems may fly very close to an object
and perform an interaction. This phase brings the variation in UAV dynamics:
thrust and drag coefficient of the propellers might change under different
proximity. At the same time, UAVs may need to operate under external
disturbances to follow time-based trajectories. Under these challenging
conditions, a standard controller approach may not handle all missions with a
fixed structure, where there may be a need to adjust its parameters for each
different case. With these motivations, practical implementation and evaluation
of an autonomous controller applied to a quadrotor UAV are proposed in this
work. A self-adaptive controller based on a composite control scheme where a
combination of sliding mode control (SMC) and evolving neuro-fuzzy control is
used. The parameter vector of the neuro-fuzzy controller is updated adaptively
based on the sliding surface of the SMC. The autonomous controller possesses a
new elastic structure, where the number of fuzzy rules keeps growing or get
pruned based on bias and variance balance. The interaction of the UAV is
experimentally evaluated in real time considering the ground effect, ceiling
effect and flight through a strong fan-generated wind while following
time-based trajectories.Comment: 18 page
Flexible collaborative transportation by a team of rotorcraft
We propose a combined method for the collaborative transportation of a
suspended payload by a team of rotorcraft. A recent distance-based
formation-motion control algorithm based on assigning distance disagreements
among robots generates the acceleration signals to be tracked by the vehicles.
In particular, the proposed method does not need global positions nor tracking
prescribed trajectories for the motion of the members of the team. The
acceleration signals are followed accurately by an Incremental Nonlinear
Dynamic Inversion controller designed for rotorcraft that measures and resists
the tensions from the payload. Our approach allows us to analyze the involved
accelerations and forces in the system so that we can calculate the worst case
conditions explicitly to guarantee a nominal performance, provided that the
payload starts at rest in the 2D centroid of the formation, and it is not under
significant disturbances. For example, we can calculate the maximum safe
deformation of the team with respect to its desired shape. We demonstrate our
method with a team of four rotorcraft carrying a suspended object two times
heavier than the maximum payload for an individual. Last but not least, our
proposed algorithm is available for the community in the open-source autopilot
Paparazzi.Comment: ICRA 2019, 6+1 page
Nonlinear Controller Design for UAVs with Time-Varying Aerodynamic Uncertainties
Unmanned Aerial Vehicles (UAVs) are here and they are here to stay. Unmanned Aviation has expanded significantly in recent years and research and development in the field of navigation and control have advanced beyond expectations. UAVs are currently being used for defense programs around the world but the range of applications is expected to grow in the near future, with civilian applications such as environmental and aerial monitoring, aerial surveillance and homeland security being some representative examples. Conventional and commercially available small-scale UAVs have limited utilization and applicability to executing specific short-duration missions because of limitations in size, payload, power supply and endurance. This fact has already marked the dawn of a new era of more powerful and versatile UAVs (e.g. morphing aircraft), able to perform a variety of missions. This dissertation presents a novel, comprehensive, step-by-step, nonlinear controller design framework for new generation, non-conventional UAVs with time-varying aerodynamic characteristics during flight. Controller design for such UAVs is a challenging task mainly due to uncertain aerodynamic parameters in the UAV mathematical model. This challenge is tackled by using and implementing μ-analysis and additive uncertainty weighting functions. The technique described herein can be generalized and applied to the class of non-conventional UAVs, seeking to address uncertainty challenges regarding the aircraft\u27s aerodynamic coefficients
Wind gust estimation for precise quasi - hovering control of quadrotor aircraft
This paper focuses on the control of quadrotor vehicles without wind sensors that are required to accurately track low-speed trajectories in the presence of moderate yet unknown wind gusts. By modeling the wind disturbance as exogenous inputs, and assuming that compensation of its effects can be achieved through quasistatic vehicle motions, this paper proposes an innovative estimation and control scheme comprising a linear dynamic filter for the estimation of such unknown inputs and requiring only position and attitude information. The filter is built upon results from Unknown Input Observer theory and allows estimation of wind and vehicle state without measurement of the wind itself. A simple feedback control law can be used to compensate for the offset position error induced by the disturbance. The proposed filter is independent of the recovery control scheme used to nullify the tracking error, as long as the corresponding applied rotor speeds are available. The solution is first checked in simulation environment by using the Robot Operating System middleware and the Gazebo simulator and then experimentally validated with a quadcopter system flying with real wind sources