1,842 research outputs found
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
Reinforcement Learning for UAV Attitude Control
Autopilot systems are typically composed of an "inner loop" providing
stability and control, while an "outer loop" is responsible for mission-level
objectives, e.g. way-point navigation. Autopilot systems for UAVs are
predominately implemented using Proportional, Integral Derivative (PID) control
systems, which have demonstrated exceptional performance in stable
environments. However more sophisticated control is required to operate in
unpredictable, and harsh environments. Intelligent flight control systems is an
active area of research addressing limitations of PID control most recently
through the use of reinforcement learning (RL) which has had success in other
applications such as robotics. However previous work has focused primarily on
using RL at the mission-level controller. In this work, we investigate the
performance and accuracy of the inner control loop providing attitude control
when using intelligent flight control systems trained with the state-of-the-art
RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy
Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate
these unknowns we first developed an open-source high-fidelity simulation
environment to train a flight controller attitude control of a quadrotor
through RL. We then use our environment to compare their performance to that of
a PID controller to identify if using RL is appropriate in high-precision,
time-critical flight control.Comment: 13 pages, 9 figure
UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle
(UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a
flying sensor but also as a tether attachment device. Two robots are connected
with a tether, allowing the UAV to anchor the tether to a structure located at
the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the
poor traversability of the UGV by not only providing a wider range of scanning
and mapping from the air, but also by allowing the UGV to climb steep terrains
with the winding of the tether. In addition, we present an autonomous framework
for the collaborative navigation and tether attachment in an unknown
environment. The UAV employs visual inertial navigation with 3D voxel mapping
and obstacle avoidance planning. The UGV makes use of the voxel map and
generates an elevation map to execute path planning based on a traversability
analysis. Furthermore, we compared the pros and cons of possible methods for
the tether anchoring from multiple points of view. To increase the probability
of successful anchoring, we evaluated the anchoring strategy with an
experiment. Finally, the feasibility and capability of our proposed system were
demonstrated by an autonomous mission experiment in the field with an obstacle
and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on
Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1
MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory
In this work, a new pre-tuning multivariable PID (Proportional Integral Derivative)
controllers method for quadrotors is put forward. A procedure based on LQR/LQG (Linear Quadratic
Regulator/Gaussian) theory is proposed for attitude and altitude control, which suposes a considerable
simplification of the design problem due to only one pretuning parameter being used. With the aim to
analyze the performance and robustness of the proposed method, a non-linear mathematical model of
the DJI-F450 quadrotor is employed, where rotors dynamics, together with sensors drift/bias properties
and noise characteristics of low-cost commercial sensors typically used in this type of applications are
considered. In order to estimate the state vector and compensate bias/drift effects in the measures,
a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude
estimation) are proposed and implemented. Performance and robustness analysis of the control system
is carried out by employing numerical simulations, which take into account the presence of uncertainty
in the plant model and external disturbances. The obtained results show the proposed controller design
method for multivariable PID controller is robust with respect to: (a) parametric uncertainty in the plant
model, (b) disturbances acting at the plant input, (c) sensors measurement and estimation errors
Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers
Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.publishedVersio
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