1,720,962 research outputs found
The role of closed-loop attitude dynamics in adaptive UAV position control
Full text linkThis paper presents the design and the stability analysis of an adaptive position controller for Unmanned Aerial Vehicles (UAVs). Considering a hierarchical control scheme, the novelty of this work is the definition of a systematic approach to design a position controller based on Model Reference Adaptive Control (MRAC) theory taking into account not-fast closed-loop attitude dynamics. After having reformulated the problem considering the attitude dynamics as pseudo-actuator, the authors exploit an existing Linear Matrix Inequality (LMI) based hedging framework designed such that the adaptation performance is not affected by the presence of actuator dynamics. Results from simulations and from experiments on a platform designed to replicate the longitudinal motion of quadrotors are provided to illustrate the performance of the proposed control scheme
Smoother-Based Iterative Learning Control for UAV Trajectory Tracking
Full text linkThis letter presents a data-based control approach to achieve high-performance trajectory tracking with Unmanned Aerial Vehicles (UAVs). We revisit an existing Iterative Learning Control (ILC) algorithm based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions. While we will specifically refer to multirotor platforms for the experimental validation, the formulation can be applied to any dynamic system (including systems with underlying feedback loops). The novelty of this work is the introduction of a smoother to estimate the repetitive disturbance to improve the learning performance. This estimator must rely on an accurate system model that has been obtained through a black-box identification procedure using the Predictor-Based Subspace Identification (PBSID) algorithm. A Monte Carlo analysis has been carried out with the aim of showing the performance improvements and limitations of the proposed algorithm with respect to existing approaches. Finally, the proposed approach has been validated through experimental activities involving a small quadrotor performing an aggressive manoeuver
Leonardo Drone Contest 2021: Politecnico di Milano team architecture
No full textIn this paper, the winning solution of the second edition of the Leonardo Drone Contest (LDC), an autonomous drone competition, is presented. The participating teams were asked to design and build an autonomous system, capable of accomplishing complex tasks in an indoor urban-like environment. To reach this goal, the designed system should be capable of navigating in a Global Navigation Satellite System (GNSS)-denied environment with autonomous decision making, online planning and collision avoidance capabilities. In this light, the authors describe the competition, its rules and objectives, as well as the proposed solution in terms of hardware, software and algorithms
H-based Transfer Learning for UAV Trajectory Tracking
No full textThis paper presents a novel transfer learning algorithm to achieve high-performance trajectory tracking with Unmanned Aerial Vehicles (UAVs). The authors exploit an existing Iterative Learning Control (ILC) algorithm based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions. However, the learning phase needed to apply such technique is related to each specific system, thus making the application of ILC poorly scalable. To overcome this limitation, the authors propose an H-optimisation-based definition of a dynamical transfer map that allows transforming the input signal learnt on a source system to the input signal needed for a target system to execute the same task. A Monte Carlo analysis has been carried out with the aim of showing the performance improvements due to the transfer knowledge. Finally, the proposed approach has been validated through experimental activities involving two different-scale quadrotors performing an aggressive manoeuvre
Leonardo Drone Contest Autonomous Drone Competition: Overview, Results, and Lessons Learned from Politecnico di Milano Team
Full text linkIn this paper, the Politecnico di Milano solutions proposed for the Leonardo Drone Contest (LDC) are presented. The Leonardo Drone Contest is an annual autonomous drone competition among universities, which has already seen the conclusion of its second edition. In each edition, the participating teams were asked to design and build an autonomous multicopter, capable of accomplishing complex tasks in an indoor urban-like environment. To reach this goal, the designed systems should be capable of navigating in a Global Navigation Satellite System (GNSS)-denied environment with autonomous decision making, online planning and collision avoidance capabilities. In this light, the authors describe the first two editions of the competition, i.e., their rules, objectives and overview of the proposed solutions. While the first edition is presented as relevant for the experience and takeaways acquired from it, the second edition solution is analyzed in detail, providing both the simulation and experimental results obtained
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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