1,721,065 research outputs found
Motor-Quadrotor Simulations based on FOC and Cuckoo PID Tuning
No full textThis paper presents advanced simulation tools for a quadrotor Unmanned Aerial Vehicle (UAV) system equipped with four six-phase Permanent Magnet Synchronous Motors (PMSMs). A Cuckoo Optimization Algorithm (COA) is designed for tuning the parameters of the decentralized PI/PID controllers of quadrotor height, quadrotor attitude angles, motor’s rotational speeds, and motors’ currents. Current control and speed control are implemented by means of the Field Oriented Control (FOC) logic. COA optimizes Square Error (SE) cost functions of the error dynamics with respect to the quadrotor’s stabilization task. The PID tuning process consists of three cascaded phases: optimization of quadrotor motion dynamics, optimization of motor speed dynamics, and optimization of motor currents dynamics. This process is repeated for different hovering and maneuvering scenarios. Sensor noise and anti-wind-up functions are also implemented. Simulations results obtained by means of MATLAB/Simulink® demonstrate the effectiveness of the approach regarding simulation and control tuning for the coupled motor-quadrotor system
Analisi topografica comparata di alcune caratteristiche della superficie di Marte e della Terra
No full textComparison between the extensional tectonic setting of the SW Thaumasia region (Mars) and the northern Ethiopian rift
No full textNovel Technology Perspectives for Urban Air Mobility Applications
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System
Full text linkThis paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach
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