An optimal control tool for trajectory generation of autonomous drones

Baroncini, Simone (2022) An optimal control tool for trajectory generation of autonomous drones. [Laurea magistrale], Università di Bologna, Corso di Studio in Automation engineering / ingegneria dell’automazione [LM-DM270], Documento full-text non disponibile
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In the recent years, autonomous aerial vehicles gained large popularity in a variety of applications in the field of automation. To accomplish various and challenging tasks the capability of generating trajectories has assumed a key role. As higher performances are sought, traditional, flatness-based trajectory generation schemes present their limitations. In these approaches the highly nonlinear dynamics of the quadrotor is, indeed, neglected. Therefore, strategies based on optimal control principles turn out to be beneficial, since in the trajectory generation process they allow the control unit to best exploit the actual dynamics, and enable the drone to perform quite aggressive maneuvers. This dissertation is then concerned with the development of an optimal control technique to generate trajectories for autonomous drones. The algorithm adopted to this end is a second-order iterative method working directly in continuous-time, which, under proper initialization, guarantees quadratic convergence to a locally optimal trajectory. At each iteration a quadratic approximation of the cost functional is minimized and a decreasing direction is then obtained as a linear-affine control law, after solving a differential Riccati equation. The algorithm has been implemented and its effectiveness has been tested on the vectored-thrust dynamical model of a quadrotor in a realistic simulative setup.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Baroncini, Simone
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Optimal Control,Trajectory Generation,Riccati Equation,Quadrotor,Drones,Optimization,Nonlinear Programming,Newton's Method,Autonomous Drones
Data di discussione della Tesi
5 Ottobre 2022

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