17 research outputs found
Modelling and Fast Terminal Sliding Mode Control for Mirror-based Pointing Systems
In this paper, we present a new discrete-time Fast Terminal Sliding Mode
(FTSM) controller for mirror-based pointing systems. We first derive the
decoupled model of those systems and then estimate the parameters using a
nonlinear least-square identification method. Based on the derived model, we
design a FTSM sliding manifold in the continuous domain. We then exploit the
Euler discretization on the designed FTSM sliding surfaces to synthesize a
discrete-time controller. Furthermore, we improve the transient dynamics of the
sliding surface by adding a linear term. Finally, we prove the stability of the
proposed controller based on the Sarpturk reaching condition. Extensive
simulations, followed by comparisons with the Terminal Sliding Mode (TSM) and
Model Predictive Control (MPC) have been carried out to evaluate the
effectiveness of the proposed approach. A comparative study with data obtained
from a real-time experiment was also conducted. The results indicate the
advantage of the proposed method over the other techniques.Comment: In Proceedings of the 15th International Conference on Control,
Automation, Robotics and Vision (ICARCV 2018
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
This paper addresses the problem of crack detection which is essential for
health monitoring of built infrastructure. Our approach includes two stages,
data collection using unmanned aerial vehicles (UAVs) and crack detection using
histogram analysis. For the data collection, a 3D model of the structure is
first created by using laser scanners. Based on the model, geometric properties
are extracted to generate way points necessary for navigating the UAV to take
images of the structure. Then, our next step is to stick together those
obtained images from the overlapped field of view. The resulting image is then
clustered by histogram analysis and peak detection. Potential cracks are
finally identified by using locally adaptive thresholds. The whole process is
automatically carried out so that the inspection time is significantly improved
while safety hazards can be minimised. A prototypical system has been developed
for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and
Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201
Deployment of UAVs for Optimal Multihop Ad-hoc Networks Using Particle Swarm Optimization and Behavior-based Control
This study proposes an approach for establishing an optimal multihop ad-hoc
network using multiple unmanned aerial vehicles (UAVs) to provide emergency
communication in disaster areas. The approach includes two stages, one uses
particle swarm optimization (PSO) to find optimal positions to deploy UAVs, and
the other uses a behavior-based controller to navigate the UAVs to their
assigned positions without colliding with obstacles in an unknown environment.
Several constraints related to the UAVs' sensing and communication ranges have
been imposed to ensure the applicability of the proposed approach in real-world
scenarios. A number of simulation experiments with data loaded from real
environments have been conducted. The results show that our proposed approach
is not only successful in establishing multihop ad-hoc routes but also meets
the requirements for real-time deployment of UAVs.Comment: In the 11th International Conference on Control, Automation and
Information Sciences (ICCAIS 2022), Hanoi, Vietna
Stable control of networked robot subject to communication delay, packet loss, and out-of-order delivery
Stabilization control of networked robot system faces uncertain factors caused by the network. Our approach for this problem consists of two steps. First, the Lyapunov stability theory is employed to derive control laws that stabilize the non-networked robot system. Those control laws are then extended to the networked robot system by implementing a predictive filter between the sensor and controller. The filter compensates influences of the network to acquire accurate estimate of the system state and consequently ensures the convergence of the control laws. The optimality of the filter in term of minimizing the mean square error is theoretically proven. Many simulations and experiments have been conducted. The result confirmed the validity of the proposed approach
Multisensor Data Fusion for Reliable Obstacle Avoidance
In this work, we propose a new approach that combines data from multiple
sensors for reliable obstacle avoidance. The sensors include two depth cameras
and a LiDAR arranged so that they can capture the whole 3D area in front of the
robot and a 2D slide around it. To fuse the data from these sensors, we first
use an external camera as a reference to combine data from two depth cameras. A
projection technique is then introduced to convert the 3D point cloud data of
the cameras to its 2D correspondence. An obstacle avoidance algorithm is then
developed based on the dynamic window approach. A number of experiments have
been conducted to evaluate our proposed approach. The results show that the
robot can effectively avoid static and dynamic obstacles of different shapes
and sizes in different environments.Comment: In the 11th International Conference on Control, Automation and
Information Sciences (ICCAIS 2022), Hanoi, Vietna