1,032 research outputs found
Agile load transportation systems using aerial robots
In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion
Controlling a drone: Comparison between a based model method and a fuzzy inference system
International audienceThe work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind
A Survey of path following control strategies for UAVs focused on quadrotors
The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer ReviewedPostprint (author's final draft
Model Predictive Control for Micro Aerial Vehicles: A Survey
This paper presents a review of the design and application of model
predictive control strategies for Micro Aerial Vehicles and specifically
multirotor configurations such as quadrotors. The diverse set of works in the
domain is organized based on the control law being optimized over linear or
nonlinear dynamics, the integration of state and input constraints, possible
fault-tolerant design, if reinforcement learning methods have been utilized and
if the controller refers to free-flight or other tasks such as physical
interaction or load transportation. A selected set of comparison results are
also presented and serve to provide insight for the selection between linear
and nonlinear schemes, the tuning of the prediction horizon, the importance of
disturbance observer-based offset-free tracking and the intrinsic robustness of
such methods to parameter uncertainty. Furthermore, an overview of recent
research trends on the combined application of modern deep reinforcement
learning techniques and model predictive control for multirotor vehicles is
presented. Finally, this review concludes with explicit discussion regarding
selected open-source software packages that deliver off-the-shelf model
predictive control functionality applicable to a wide variety of Micro Aerial
Vehicle configurations
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