Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone
The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations durin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/10/5/418 |
| _version_ | 1797818828086837248 |
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| author | Manuel Mariani Simone Fiori |
| author_facet | Manuel Mariani Simone Fiori |
| author_sort | Manuel Mariani |
| collection | DOAJ |
| description | The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter’s current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function’s definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times. |
| first_indexed | 2024-03-13T09:13:51Z |
| format | Article |
| id | doaj.art-1bf060be0dcd4f71aff17768d5e0779b |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-13T09:13:51Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-1bf060be0dcd4f71aff17768d5e0779b2023-05-26T13:20:38ZengMDPI AGAerospace2226-43102023-04-011041841810.3390/aerospace10050418Design and Simulation of a Neuroevolutionary Controller for a Quadcopter DroneManuel Mariani0Simone Fiori1School of Artificial Intelligence, Università degli Studi di Bologna, Viale del Risorgimento, 2, 40136 Bologna, ItalyDipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, ItalyThe problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter’s current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function’s definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times.https://www.mdpi.com/2226-4310/10/5/418neuro-evolutionary controlquadcopterpath-following |
| spellingShingle | Manuel Mariani Simone Fiori Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone Aerospace neuro-evolutionary control quadcopter path-following |
| title | Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone |
| title_full | Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone |
| title_fullStr | Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone |
| title_full_unstemmed | Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone |
| title_short | Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone |
| title_sort | design and simulation of a neuroevolutionary controller for a quadcopter drone |
| topic | neuro-evolutionary control quadcopter path-following |
| url | https://www.mdpi.com/2226-4310/10/5/418 |
| work_keys_str_mv | AT manuelmariani designandsimulationofaneuroevolutionarycontrollerforaquadcopterdrone AT simonefiori designandsimulationofaneuroevolutionarycontrollerforaquadcopterdrone |