Tesi di laurea magistrale
A Novel Nonlinear Model Predictive Control for coordinating a UAV Swarm
Corso di studi
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
relatore Prof. Innocenti, Mario
- coordinated control
- uav swarm
- particle swarm optimization
- nonlinear model predictive control
Data inizio appello
Data di rilascio
This thesis project addresses the problem of coordinating the motion of a multiagents system for a real time application. The case study is a Unmanned Aerial Vehicles (UAV) swarm moving on a not completely known map with pop-up obstacles to be avoided. The main<br>concept of coordination is discussed, providing a general procedure to describe the environment and mission goals through a proper optimization problem. Relative distances<br>are used to describe the tasks, as in a standard formation control. The usage of a minimal task description permits the swarm to have more degree of freedom to achieve the optimal configuration. Dealing with not completely known environments, where the robot<br>has to react to sensory data as quickly as possible, a real time algorithm is necessary. For this reason, the controller design needs to properly face the computational burden and environments variations issues. Furthermore, the algorithm should be distributed.<br>The report presents a novel approach that allows to easily deal with nonlinear models<br>and very complex, time variant cost function. A distributed Non Linear Model Predictive Control is implemented and a Particle Swarm Optimization is used to solve the<br>optimization problem.
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